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1. Ahmad M, Helms V, Kalinina OV, Lengauer T: Elucidating the Energetic Contributions to the Binding Free Energy. The Journal of Chemical Physics 2017, 146.
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@article{Ahmad2017, TITLE = {Elucidating the Energetic Contributions to the Binding Free Energy}, AUTHOR = {Ahmad, Mazen and Helms, Volkhard and Kalinina, Olga V. and Lengauer, Thomas}, LANGUAGE = {eng}, ISSN = {0021-9606}, DOI = {10.1063/1.4973349}, PUBLISHER = {American Institute of Physics}, ADDRESS = {Woodbury, N.Y.}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, JOURNAL = {The Journal of Chemical Physics}, VOLUME = {146}, NUMBER = {1}, EID = {014105}, }
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%0 Journal Article %A Ahmad, Mazen %A Helms, Volkhard %A Kalinina, Olga V. %A Lengauer, Thomas %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Elucidating the Energetic Contributions to the Binding Free Energy : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-3A74-2 %R 10.1063/1.4973349 %7 2017 %D 2017 %J The Journal of Chemical Physics %O J. Chem. Phys. %V 146 %N 1 %Z sequence number: 014105 %I American Institute of Physics %C Woodbury, N.Y. %@ false
2. Albrecht F, List M, Bock C, Lengauer T: DeepBlueR: Large-scale Epigenomic Analysis in R. Bioinformatics 2017, 33.
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@article{AlbrechtBioinformatics2017, TITLE = {{DeepBlueR}: {L}arge-scale Epigenomic Analysis in {R}}, AUTHOR = {Albrecht, Felipe and List, Markus and Bock, Christoph and Lengauer, Thomas}, LANGUAGE = {eng}, ISSN = {1367-4803}, DOI = {10.1093/bioinformatics/btx099}, PUBLISHER = {Oxford University Press}, ADDRESS = {Oxford}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, JOURNAL = {Bioinformatics}, VOLUME = {33}, NUMBER = {13}, PAGES = {2063--2064}, EID = {btx099}, }
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%0 Journal Article %A Albrecht, Felipe %A List, Markus %A Bock, Christoph %A Lengauer, Thomas %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T DeepBlueR: Large-scale Epigenomic Analysis in R : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-8AB4-4 %R 10.1093/bioinformatics/btx099 %7 2017-02-22 %D 2017 %J Bioinformatics %V 33 %N 13 %& 2063 %P 2063 - 2064 %Z sequence number: btx099 %I Oxford University Press %C Oxford %@ false
3. Alcaraz N, List M, Batra R, Vandin F, Ditzel HJ, Baumbach J: De novo Pathway-based Biomarker Identification. Nucleic Acids Research 2017, 45.
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@article{Alcaraz2017, TITLE = {\textit{De novo} pathway-based biomarker identification}, AUTHOR = {Alcaraz, Nicolas and List, Markus and Batra, Richa and Vandin, Fabio and Ditzel, Henrik J. and Baumbach, Jan}, LANGUAGE = {eng}, ISSN = {0305-1048}, DOI = {https://doi.org/10.1093/nar/gkx642}, PUBLISHER = {Oxford University Press}, ADDRESS = {Oxford}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, JOURNAL = {Nucleic Acids Research}, VOLUME = {45}, NUMBER = {16}, EID = {e151}, }
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%0 Journal Article %A Alcaraz, Nicolas %A List, Markus %A Batra, Richa %A Vandin, Fabio %A Ditzel, Henrik J. %A Baumbach, Jan %+ External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T De novo Pathway-based Biomarker Identification : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-DDE0-C %R https://doi.org/10.1093/nar/gkx642 %7 2017 %D 2017 %J Nucleic Acids Research %O Nucleic Acids Res %V 45 %N 16 %Z sequence number: e151 %I Oxford University Press %C Oxford %@ false
4. Almeida D, Skov I, Silva A, Vandin F, Tan Q, Röttger R, Baumbach J: Efficient Detection of Differentially Methylated Regions using DiMmeR. Bioinformatics 2017, 33.
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@article{Almeida2017, TITLE = {Efficient Detection of Differentially Methylated Regions using {DiMmeR}}, AUTHOR = {Almeida, Diogo and Skov, Ida and Silva, Artur and Vandin, Fabio and Tan, Qihua and R{\"o}ttger, Richard and Baumbach, Jan}, LANGUAGE = {eng}, ISSN = {1367-4803}, DOI = {10.1093/bioinformatics/btw657}, PUBLISHER = {Oxford University Press}, ADDRESS = {Oxford}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, JOURNAL = {Bioinformatics}, VOLUME = {33}, NUMBER = {4}, PAGES = {549--551}, }
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%0 Journal Article %A Almeida, Diogo %A Skov, Ida %A Silva, Artur %A Vandin, Fabio %A Tan, Qihua %A Röttger, Richard %A Baumbach, Jan %+ External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Efficient Detection of Differentially Methylated Regions using DiMmeR : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-ECA5-7 %R 10.1093/bioinformatics/btw657 %7 2016 %D 2017 %J Bioinformatics %V 33 %N 4 %& 549 %P 549 - 551 %I Oxford University Press %C Oxford %@ false
5. Arras L, Horn F, Montavon G, Müller K-R, Samek W: “What is Relevant in a Text Document?”: An Interpretable Machine Learning Approach. PLoS One 2017, 12.
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@article{Arras2017, TITLE = {"What is relevant in a text document?": {An} interpretable machine learning approach}, AUTHOR = {Arras, Leila and Horn, Franziska and Montavon, Gr{\'e}goire and M{\"u}ller, Klaus-Robert and Samek, Wojciech}, LANGUAGE = {eng}, ISSN = {1932-6203}, DOI = {10.1371/journal.pone.0181142}, PUBLISHER = {Public Library of Science}, ADDRESS = {San Francisco, CA}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, JOURNAL = {PLoS One}, VOLUME = {12}, NUMBER = {8}, EID = {e0181142}, }
Endnote
%0 Journal Article %A Arras, Leila %A Horn, Franziska %A Montavon, Grégoire %A Müller, Klaus-Robert %A Samek, Wojciech %+ External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations %T "What is Relevant in a Text Document?": An Interpretable Machine Learning Approach : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-DC8E-E %R 10.1371/journal.pone.0181142 %2 PMC5553725 %7 2017-08-11 %D 2017 %8 11.08.2017 %J PLoS One %V 12 %N 8 %Z sequence number: e0181142 %I Public Library of Science %C San Francisco, CA %@ false
6. Bader J, Däumer M, Schöni-Affolter F, Böni J, Gorgievski-Hrisoho M, Martinetti G, Thielen A, Klimkait T: Therapeutic Immune Recovery and Reduction of CXCR4-Tropic HIV-1. Clinical Infectious Diseases 2017, 64.
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@article{Bader2017, TITLE = {Therapeutic Immune Recovery and Reduction of {CXCR4}-Tropic {HIV}-1}, AUTHOR = {Bader, Jo{\"e}lle and D{\"a}umer, Martin and Sch{\"o}ni-Affolter, Franziska and B{\"o}ni, J{\"u}rg and Gorgievski-Hrisoho, Meri and Martinetti, Gladys and Thielen, Alexander and Klimkait, Thomas}, LANGUAGE = {eng}, ISSN = {1058-4838}, DOI = {10.1093/cid/ciw737}, PUBLISHER = {The University of Chicago Press}, ADDRESS = {Chicago, IL}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, JOURNAL = {Clinical Infectious Diseases}, VOLUME = {64}, NUMBER = {3}, PAGES = {295--300}, }
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%0 Journal Article %A Bader, Joëlle %A Däumer, Martin %A Schöni-Affolter, Franziska %A Böni, Jürg %A Gorgievski-Hrisoho, Meri %A Martinetti, Gladys %A Thielen, Alexander %A Klimkait, Thomas %+ External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations %T Therapeutic Immune Recovery and Reduction of CXCR4-Tropic HIV-1 : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-ECAE-6 %R 10.1093/cid/ciw737 %7 2016 %D 2017 %J Clinical Infectious Diseases %V 64 %N 3 %& 295 %P 295 - 300 %I The University of Chicago Press %C Chicago, IL %@ false
7. Batra R, Alcaraz N, Gitzhofer K, Pauling J, Ditzel HJ, Hellmuth M, Baumbach J, List M: On the Performance of De Novo Pathway Enrichment. njp Systems Biology and Applications 2017, 3.
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@article{Baumbachnjp2016, TITLE = {On the Performance of De Novo Pathway Enrichment}, AUTHOR = {Batra, Richa and Alcaraz, Nicolas and Gitzhofer, Kevin and Pauling, Josch and Ditzel, Henrik J. and Hellmuth, Marc and Baumbach, Jan and List, Markus}, LANGUAGE = {eng}, DOI = {10.1038/s41540-017-0007-2}, PUBLISHER = {Nature Publishing Group}, ADDRESS = {London}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, JOURNAL = {njp Systems Biology and Applications}, VOLUME = {3}, PAGES = {1--8}, EID = {6}, }
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%0 Journal Article %A Batra, Richa %A Alcaraz, Nicolas %A Gitzhofer, Kevin %A Pauling, Josch %A Ditzel, Henrik J. %A Hellmuth, Marc %A Baumbach, Jan %A List, Markus %+ External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T On the Performance of De Novo Pathway Enrichment : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-2039-6 %R 10.1038/s41540-017-0007-2 %7 2017 %D 2017 %J njp Systems Biology and Applications %V 3 %& 1 %P 1 - 8 %Z sequence number: 6 %I Nature Publishing Group %C London
8. Brockherde F, Vogt L, Li L, Tuckerman ME, Burke K, Müller K-R: Bypassing the Kohn-Sham Equations with Machine Learning. Nature Communications 2017, 8.
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@article{Brockherde2017, TITLE = {Bypassing the {Kohn}-{Sham} equations with machine learning}, AUTHOR = {Brockherde, Felix and Vogt, Leslie and Li, Li and Tuckerman, Mark E. and Burke, Kieron and M{\"u}ller, Klaus-Robert}, LANGUAGE = {eng}, ISSN = {2041-1723}, DOI = {10.1038/s41467-017-00839-3}, PUBLISHER = {Nature Publishing Group}, ADDRESS = {London}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, JOURNAL = {Nature Communications}, VOLUME = {8}, EID = {872}, }
Endnote
%0 Journal Article %A Brockherde, Felix %A Vogt, Leslie %A Li, Li %A Tuckerman, Mark E. %A Burke, Kieron %A Müller, Klaus-Robert %+ External Organizations External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Bypassing the Kohn-Sham Equations with Machine Learning : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-177E-5 %R 10.1038/s41467-017-00839-3 %7 2017 %D 2017 %J Nature Communications %O Nat. Commun. %V 8 %Z sequence number: 872 %I Nature Publishing Group %C London %@ false
9. Busch CJ-L, Hendrikx T, Weismann D, Jäckel S, Walenbergh SMA, Rendeiro AF, Weißer J, Puhm F, Hladik A, Göderle L, Papac-Milicevic N, Haas G, Millischer V, Subramaniam S, Knapp S, Bennett KL, Bock C, Reinhardt C, Shiri-Sverdlov R, Binder CJ: Malondialdehyde Epitopes Are Sterile Mediators of Hepatic Inflammation in Hypercholesterolemic Mice. Hepatology 2017, 65.
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@article{Busch2017, TITLE = {Malondialdehyde Epitopes Are Sterile Mediators of Hepatic Inflammation in Hypercholesterolemic Mice}, AUTHOR = {Busch, Clara Jana-Lui and Hendrikx, Tim and Weismann, David and J{\"a}ckel, Sven and Walenbergh, Sofie M. A. and Rendeiro, Andr{\'e} F. and Wei{\ss}er, Juliane and Puhm, Florian and Hladik, Anastasiya and G{\"o}derle, Laura and Papac-Milicevic, Nikolina and Haas, Gerald and Millischer, Vincent and Subramaniam, Saravanan and Knapp, Sylvia and Bennett, Keiryn L. and Bock, Christoph and Reinhardt, Christoph and Shiri-Sverdlov, Ronit and Binder, Christoph J.}, LANGUAGE = {eng}, ISSN = {0270-9139}, DOI = {10.1002/hep.28970}, PUBLISHER = {AASLD}, ADDRESS = {Alexandria, VA}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, JOURNAL = {Hepatology}, VOLUME = {65}, NUMBER = {4}, PAGES = {1181--1195}, }
Endnote
%0 Journal Article %A Busch, Clara Jana-Lui %A Hendrikx, Tim %A Weismann, David %A Jäckel, Sven %A Walenbergh, Sofie M. A. %A Rendeiro, André F. %A Weißer, Juliane %A Puhm, Florian %A Hladik, Anastasiya %A Göderle, Laura %A Papac-Milicevic, Nikolina %A Haas, Gerald %A Millischer, Vincent %A Subramaniam, Saravanan %A Knapp, Sylvia %A Bennett, Keiryn L. %A Bock, Christoph %A Reinhardt, Christoph %A Shiri-Sverdlov, Ronit %A Binder, Christoph J. %+ External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations %T Malondialdehyde Epitopes Are Sterile Mediators of Hepatic Inflammation in Hypercholesterolemic Mice : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-576B-C %R 10.1002/hep.28970 %7 2017 %D 2017 %J Hepatology %V 65 %N 4 %& 1181 %P 1181 - 1195 %I AASLD %C Alexandria, VA %@ false
10. Caskey M, Schoofs T, Gruell H, Settler A, Karagounis T, Kreider EF, Murrell B, Pfeifer N, Nogueira L, Oliveira TY, Learn GH, Cohen YZ, Lehmann C, Gillor D, Shimeliovich I, Unson-O’Brien C, Weiland D, Robles A, Kümmerle T, Wyen C, Levin R, Witmer-Pack M, Eren K, Ignacio C, Kiss S, Jr APW, Mouquet H, Zingman BS, Gulick RM, Keler T, et al.: Antibody 10-1074 Suppresses Viremia in HIV-1-infected Individuals. Nature Medicine 2017, 23.
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@article{Pfeifernature2017, TITLE = {Antibody 10-1074 Suppresses Viremia in {HIV}-1-infected Individuals}, AUTHOR = {Caskey, Marina and Schoofs, Till and Gruell, Henning and Settler, Allison and Karagounis, Theodora and Kreider, Edward F and Murrell, Ben and Pfeifer, Nico and Nogueira, Lilian and Oliveira, Thiago Y and Learn, Gerald H and Cohen, Yehuda Z and Lehmann, Clara and Gillor, Daniel and Shimeliovich, Irina and Unson-O{\textquoteright}Brien, Cecilia and Weiland, Daniela and Robles, Alexander and K{\"u}mmerle, Tim and Wyen, Christoph and Levin, Rebeka and Witmer-Pack, Maggi and Eren, Kemal and Ignacio, Caroline and Kiss, Szilard and Jr, Anthony P West and Mouquet, Hugo and Zingman, Barry S and Gulick, Roy M and Keler, Tibor and Bjorkman, Pamela J and Seaman, Michael S and Hahn, Beatrice H and F{\"a}tkenheuer, Gerd and Schlesinger, Sarah J and Nussenzweig, Michel C and Klein, Florian}, LANGUAGE = {eng}, ISSN = {1078-8956}, DOI = {10.1038/nm.4268}, PUBLISHER = {Nature Pub. Co.}, ADDRESS = {New York, NY}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, JOURNAL = {Nature Medicine}, VOLUME = {23}, NUMBER = {2}, PAGES = {185--191}, }
Endnote
%0 Journal Article %A Caskey, Marina %A Schoofs, Till %A Gruell, Henning %A Settler, Allison %A Karagounis, Theodora %A Kreider, Edward F %A Murrell, Ben %A Pfeifer, Nico %A Nogueira, Lilian %A Oliveira, Thiago Y %A Learn, Gerald H %A Cohen, Yehuda Z %A Lehmann, Clara %A Gillor, Daniel %A Shimeliovich, Irina %A Unson-O’Brien, Cecilia %A Weiland, Daniela %A Robles, Alexander %A Kümmerle, Tim %A Wyen, Christoph %A Levin, Rebeka %A Witmer-Pack, Maggi %A Eren, Kemal %A Ignacio, Caroline %A Kiss, Szilard %A Jr, Anthony P West %A Mouquet, Hugo %A Zingman, Barry S %A Gulick, Roy M %A Keler, Tibor %A Bjorkman, Pamela J %A Seaman, Michael S %A Hahn, Beatrice H %A Fätkenheuer, Gerd %A Schlesinger, Sarah J %A Nussenzweig, Michel C %A Klein, Florian %+ External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations %T Antibody 10-1074 Suppresses Viremia in HIV-1-infected Individuals : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-7F0C-8 %R 10.1038/nm.4268 %7 2017-01-16 %D 2017 %J Nature Medicine %O Nat. Med. %V 23 %N 2 %& 185 %P 185 - 191 %I Nature Pub. Co. %C New York, NY %@ false
11. Chmiela S, Tkatchenko A, Sauceda HE, Poltavsky I, Schütt KT, Müller K-R: Machine learning of accurate energy-conserving molecular force fields. Science Advances 2017, 3.
Abstract
Using conservation of energy - a fundamental property of closed classical and quantum mechanical systems - we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio molecular dynamics (AIMD) trajectories. The GDML implementation is able to reproduce global potentialenergy surfaces of intermediate-size molecules with an accuracy of 0.3 kcal/mol<sup>-1</sup> for energies and 1 kcal mol<sup>-1</sup> Å̊<sup>−1</sup> for atomic forces using only 1000 conformational geometries for training. We demonstrate this accuracy for AIMD trajectories of molecules, including benzene, toluene, naphthalene, ethanol, uracil, and aspirin. The challenge of constructing conservative force fields is accomplished in our work by learning in a Hilbert space of vector-valued functions that obey the law of energy conservation. The GDML approach enables quantitative molecular dynamics simulations for molecules at a fraction of cost of explicit AIMD calculations, thereby allowing the construction of efficient force fields with the accuracy and transferability of high-level ab initio methods.
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@article{Chmiela2017, TITLE = {Machine learning of accurate energy-conserving molecular force fields}, AUTHOR = {Chmiela, Stefan and Tkatchenko, Alexandre and Sauceda, Huziel E. and Poltavsky, Igor and Sch{\"u}tt, Kristof T. and M{\"u}ller, Klaus-Robert}, LANGUAGE = {eng}, DOI = {10.1126/sciadv.1603015}, PUBLISHER = {AAAS}, ADDRESS = {Washington}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Using conservation of energy -- a fundamental property of closed classical and quantum mechanical systems -- we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio molecular dynamics (AIMD) trajectories. The GDML implementation is able to reproduce global potentialenergy surfaces of intermediate-size molecules with an accuracy of 0.3 kcal/mol<sup>-1</sup> for energies and 1 kcal mol<sup>-1</sup> {\AA}\r <sup>{\textminus}1</sup> for atomic forces using only 1000 conformational geometries for training. We demonstrate this accuracy for AIMD trajectories of molecules, including benzene, toluene, naphthalene, ethanol, uracil, and aspirin. The challenge of constructing conservative force fields is accomplished in our work by learning in a Hilbert space of vector-valued functions that obey the law of energy conservation. The GDML approach enables quantitative molecular dynamics simulations for molecules at a fraction of cost of explicit AIMD calculations, thereby allowing the construction of efficient force fields with the accuracy and transferability of high-level ab initio methods.}, JOURNAL = {Science Advances}, VOLUME = {3}, NUMBER = {5}, EID = {e1603015}, }
Endnote
%0 Journal Article %A Chmiela, Stefan %A Tkatchenko, Alexandre %A Sauceda, Huziel E. %A Poltavsky, Igor %A Sch&#252;tt, Kristof T. %A M&#252;ller, Klaus-Robert %+ External Organizations External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Machine learning of accurate energy-conserving molecular force fields : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-590A-8 %R 10.1126/sciadv.1603015 %2 PMC5419702 %7 2017-05-05 %D 2017 %8 05.05.2017 %* Review method: peer-reviewed %X Using conservation of energy - a fundamental property of closed classical and quantum mechanical systems - we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio molecular dynamics (AIMD) trajectories. The GDML implementation is able to reproduce global potentialenergy surfaces of intermediate-size molecules with an accuracy of 0.3 kcal/mol<sup>-1</sup> for energies and 1 kcal mol<sup>-1</sup> &#197;&#778;<sup>&#8722;1</sup> for atomic forces using only 1000 conformational geometries for training. We demonstrate this accuracy for AIMD trajectories of molecules, including benzene, toluene, naphthalene, ethanol, uracil, and aspirin. The challenge of constructing conservative force fields is accomplished in our work by learning in a Hilbert space of vector-valued functions that obey the law of energy conservation. The GDML approach enables quantitative molecular dynamics simulations for molecules at a fraction of cost of explicit AIMD calculations, thereby allowing the construction of efficient force fields with the accuracy and transferability of high-level ab initio methods. %J Science Advances %O Sci. Adv. %V 3 %N 5 %Z sequence number: e1603015 %I AAAS %C Washington
12. Datlinger P, Rendeiro AF, Schmidl C, Krausgruber T, Traxler P, Klughammer J, Schuster LC, Kuchler A, Alpar D, Bock C: Pooled CRISPR Screening with Single-cell Transcriptome Readout. Nature Methods 2017, 14.
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@article{Bocknature2017, TITLE = {Pooled {CRISPR} Screening with Single-cell Transcriptome Readout}, AUTHOR = {Datlinger, Paul and Rendeiro, Andr{\'e} F and Schmidl, Christian and Krausgruber, Thomas and Traxler, Peter and Klughammer, Johanna and Schuster, Linda C and Kuchler, Amelie and Alpar, Donat and Bock, Christoph}, LANGUAGE = {eng}, ISSN = {1548-7091}, DOI = {10.1038/nmeth.4177}, PUBLISHER = {Nature Pub. Group}, ADDRESS = {New York, NY}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, JOURNAL = {Nature Methods}, VOLUME = {14}, NUMBER = {3}, PAGES = {297--301}, }
Endnote
%0 Journal Article %A Datlinger, Paul %A Rendeiro, Andr&#233; F %A Schmidl, Christian %A Krausgruber, Thomas %A Traxler, Peter %A Klughammer, Johanna %A Schuster, Linda C %A Kuchler, Amelie %A Alpar, Donat %A Bock, Christoph %+ External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Pooled CRISPR Screening with Single-cell Transcriptome Readout : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-532B-2 %R 10.1038/nmeth.4177 %7 2017 %D 2017 %J Nature Methods %O Nature methods %V 14 %N 3 %& 297 %P 297 - 301 %I Nature Pub. Group %C New York, NY %@ false
13. Demirci MDS, Baumbach J, Allmer J: On the Performance of pre-microRNA Detection Algorithms. Nature Communications 2017, 8.
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@article{Demirci2017, TITLE = {On the performance of pre-micro{RNA} detection algorithms}, AUTHOR = {Demirci, M{\"u}{\c s}erref Duygu Sa{\c c}ar and Baumbach, Jan and Allmer, Jens}, LANGUAGE = {eng}, ISSN = {2041-1723}, DOI = {10.1038/s41467-017-00403-z}, PUBLISHER = {Nature Publishing Group}, ADDRESS = {London}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, JOURNAL = {Nature Communications}, VOLUME = {8}, EID = {330}, }
Endnote
%0 Journal Article %A Demirci, M&#252;&#351;erref Duygu Sa&#231;ar %A Baumbach, Jan %A Allmer, Jens %+ External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations %T On the Performance of pre-microRNA Detection Algorithms : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-DC7E-1 %R 10.1038/s41467-017-00403-z %2 PMC5571158 %7 2017-08-24 %D 2017 %8 24.08.2017 %J Nature Communications %O Nat. Commun. %V 8 %Z sequence number: 330 %I Nature Publishing Group %C London %@ false
14. Ebler J, Schönhuth A, Marschall T: Genotyping of Inversions and Tandem Duplications. Bioinformatics 2017.
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@article{Marshallbio17, TITLE = {Genotyping of Inversions and Tandem Duplications}, AUTHOR = {Ebler, Jana and Sch{\"o}nhuth, Alexander and Marschall, Tobias}, LANGUAGE = {eng}, ISSN = {1367-4803}, DOI = {10.1093/bioinformatics/btx020}, PUBLISHER = {Oxford University Press}, ADDRESS = {Oxford}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, JOURNAL = {Bioinformatics}, EID = {btx020}, }
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%0 Journal Article %A Ebler, Jana %A Sch&#246;nhuth, Alexander %A Marschall, Tobias %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Genotyping of Inversions and Tandem Duplications : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-468F-7 %R 10.1093/bioinformatics/btx020 %7 2017-02-07 %D 2017 %8 07.02.2017 %J Bioinformatics %Z sequence number: btx020 %I Oxford University Press %C Oxford %@ false
15. Gress A, Ramensky V, Kalinina OV: Spatial Distribution of Disease-associated Variants in Three-dimensional Structures of Protein Complexes. Oncogenesis 2017, 6.
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@article{Gress_2017, TITLE = {Spatial Distribution of Disease-associated Variants in Three-dimensional Structures of Protein Complexes}, AUTHOR = {Gress, Alexander and Ramensky, V. and Kalinina, Olga V.}, LANGUAGE = {eng}, ISSN = {2157-9024}, DOI = {10.1038/oncsis.2017.79}, PUBLISHER = {Nature Publishing}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, JOURNAL = {Oncogenesis}, VOLUME = {6}, NUMBER = {9}, EID = {e380}, }
Endnote
%0 Journal Article %A Gress, Alexander %A Ramensky, V. %A Kalinina, Olga V. %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Spatial Distribution of Disease-associated Variants in Three-dimensional Structures of Protein Complexes : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-24CF-8 %R 10.1038/oncsis.2017.79 %2 PMC5623905 %7 2017 %D 2017 %J Oncogenesis %V 6 %N 9 %Z sequence number: e380 %I Nature Publishing %@ false
16. Hake A, Pfeifer N: Prediction of HIV-1 Sensitivity to Broadly Neutralizing Antibodies Shows a Trend towards Resistance over Time. PLoS Computational Biology 2017, 13.
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@article{HakePfeifer2017, TITLE = {Prediction of {HIV}-1 Sensitivity to Broadly Neutralizing Antibodies Shows a Trend towards Resistance over Time}, AUTHOR = {Hake, Anna and Pfeifer, Nico}, LANGUAGE = {eng}, ISSN = {1553-734X}, DOI = {10.1371/journal.pcbi.1005789}, PUBLISHER = {Public Library of Science}, ADDRESS = {San Francisco, CA}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, JOURNAL = {PLoS Computational Biology}, VOLUME = {13}, NUMBER = {10}, EID = {e1005789}, }
Endnote
%0 Journal Article %A Hake, Anna %A Pfeifer, Nico %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Prediction of HIV-1 Sensitivity to Broadly Neutralizing Antibodies Shows a Trend towards Resistance over Time : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-3127-A %R 10.1371/journal.pcbi.1005789 %7 2017 %D 2017 %J PLoS Computational Biology %V 13 %N 10 %Z sequence number: e1005789 %I Public Library of Science %C San Francisco, CA %@ false
17. Hashemi S, Dalini AN, Jalali A, Banaei-Moghaddam AM, Razaghi-Moghadam Z: Cancerouspdomains: Comprehensive Analysis of Cancer Type-specific Recurrent Somatic Mutations in Proteins and Domains. BMC Bioinformatics 2017, 18.
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@article{Hashemi2017, TITLE = {Cancerouspdomains: {C}omprehensive analysis of cancer type-specific recurrent somatic mutations in proteins and domains}, AUTHOR = {Hashemi, Seirana and Dalini, Abbas Nowzari and Jalali, Adrin and Banaei-Moghaddam, Ali Mohammad and Razaghi-Moghadam, Zahra}, LANGUAGE = {eng}, ISSN = {1471-2105}, DOI = {10.1186/s12859-017-1779-5}, PUBLISHER = {BioMed Central}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, JOURNAL = {BMC Bioinformatics}, VOLUME = {18}, EID = {370}, }
Endnote
%0 Journal Article %A Hashemi, Seirana %A Dalini, Abbas Nowzari %A Jalali, Adrin %A Banaei-Moghaddam, Ali Mohammad %A Razaghi-Moghadam, Zahra %+ External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Cancerouspdomains: Comprehensive Analysis of Cancer Type-specific Recurrent Somatic Mutations in Proteins and Domains : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-DDC4-C %R 10.1186/s12859-017-1779-5 %7 2017 %D 2017 %J BMC Bioinformatics %V 18 %Z sequence number: 370 %I BioMed Central %@ false
18. Heger E, Kaiser R, Knops E, Neumann-Fraune M, Pironti A, Lengauer T, Walter H, Sierra S: Results of the First International HIV-1 Coreceptor Proficiency Panel Test. Journal of Clinical Virology 2017, 93.
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@article{Heger2017, TITLE = {Results of the First International {HIV}-1 Coreceptor Proficiency Panel Test}, AUTHOR = {Heger, Eva and Kaiser, Rolf and Knops, Elena and Neumann-Fraune, Maria and Pironti, Alejandro and Lengauer, Thomas and Walter, Hauke and Sierra, Saleta}, LANGUAGE = {eng}, ISSN = {1386-6532}, DOI = {10.1016/j.jcv.2017.06.002}, PUBLISHER = {Elsevier}, ADDRESS = {Amsterdam}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, JOURNAL = {Journal of Clinical Virology}, VOLUME = {93}, PAGES = {53--56}, }
Endnote
%0 Journal Article %A Heger, Eva %A Kaiser, Rolf %A Knops, Elena %A Neumann-Fraune, Maria %A Pironti, Alejandro %A Lengauer, Thomas %A Walter, Hauke %A Sierra, Saleta %+ External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Results of the First International HIV-1 Coreceptor Proficiency Panel Test : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-0189-7 %R 10.1016/j.jcv.2017.06.002 %7 2017 %D 2017 %J Journal of Clinical Virology %V 93 %& 53 %P 53 - 56 %I Elsevier %C Amsterdam %@ false
19. Jühling F, Bandiera S, Hamdane N, Thumann C, Durand SC, Saghire H. E, Davidson I, Habersetzer F, Pessaux P, Bardeesy N, Schmidl C, Bock C, Hoshida Y, Zeisel MB, Baumert TF: Hepatitis C Virus-induced Epigenetic and Transcriptional Changes Persist Post Cure. Journal of Hepatology 2017, 66.
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@article{Juehling2017, TITLE = {Hepatitis {C} Virus-induced Epigenetic and Transcriptional Changes Persist Post Cure}, AUTHOR = {J{\"u}hling, F. and Bandiera, S. and Hamdane, N. and Thumann, C. and Durand, S. C. and Saghire, H .E. and Davidson, I. and Habersetzer, F. and Pessaux, P. and Bardeesy, N. and Schmidl, C. and Bock, Christoph and Hoshida, Y. and Zeisel, M. B. and Baumert, T. F.}, LANGUAGE = {eng}, ISBN = {0168-8278}, URL = {http://dx.doi.org/10.1016/S0168-8278(17)30304-5}, DOI = {10.1016/S0168-8278(17)30304-5}, PUBLISHER = {Elsevier}, ADDRESS = {Amsterdam}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, JOURNAL = {Journal of Hepatology}, VOLUME = {66}, NUMBER = {1}, PAGES = {S21--S21}, }
Endnote
%0 Journal Article %A J&#252;hling, F. %A Bandiera, S. %A Hamdane, N. %A Thumann, C. %A Durand, S. C. %A Saghire, H .E. %A Davidson, I. %A Habersetzer, F. %A Pessaux, P. %A Bardeesy, N. %A Schmidl, C. %A Bock, Christoph %A Hoshida, Y. %A Zeisel, M. B. %A Baumert, T. F. %+ external external external external external external external external external external external Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society external external external %T Hepatitis C Virus-induced Epigenetic and Transcriptional Changes Persist Post Cure : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-7861-8 %U http://dx.doi.org/10.1016/S0168-8278(17)30304-5 %R 10.1016/S0168-8278(17)30304-5 %7 2017 %D 2017 %J Journal of Hepatology %V 66 %N 1 %& S21 %P S21 - S21 %I Elsevier %C Amsterdam %@ 0168-8278
20. Selecting Optimal Minimum Spanning Trees that Share a Topological Correspondence with Phylogenetic Trees [http://arxiv.org/abs/1701.02844]
(arXiv: 1701.02844)
Abstract
Choi et. al (2011) introduced a minimum spanning tree (MST)-based method called CLGrouping, for constructing tree-structured probabilistic graphical models, a statistical framework that is commonly used for inferring phylogenetic trees. While CLGrouping works correctly if there is a unique MST, we observe an indeterminacy in the method in the case that there are multiple MSTs. In this work we remove this indeterminacy by introducing so-called vertex-ranked MSTs. We note that the effectiveness of CLGrouping is inversely related to the number of leaves in the MST. This motivates the problem of finding a vertex-ranked MST with the minimum number of leaves (MLVRMST). We provide a polynomial time algorithm for the MLVRMST problem, and prove its correctness for graphs whose edges are weighted with tree-additive distances.
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@online{Kalaghatgi2017, TITLE = {Selecting Optimal Minimum Spanning Trees that Share a Topological Correspondence with Phylogenetic Trees}, AUTHOR = {Kalaghatgi, Prabhav and Lengauer, Thomas}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1701.02844}, EPRINT = {1701.02844}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Choi et. al (2011) introduced a minimum spanning tree (MST)-based method called CLGrouping, for constructing tree-structured probabilistic graphical models, a statistical framework that is commonly used for inferring phylogenetic trees. While CLGrouping works correctly if there is a unique MST, we observe an indeterminacy in the method in the case that there are multiple MSTs. In this work we remove this indeterminacy by introducing so-called vertex-ranked MSTs. We note that the effectiveness of CLGrouping is inversely related to the number of leaves in the MST. This motivates the problem of finding a vertex-ranked MST with the minimum number of leaves (MLVRMST). We provide a polynomial time algorithm for the MLVRMST problem, and prove its correctness for graphs whose edges are weighted with tree-additive distances.}, }
Endnote
%0 Report %A Kalaghatgi, Prabhav %A Lengauer, Thomas %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Selecting Optimal Minimum Spanning Trees that Share a Topological Correspondence with Phylogenetic Trees : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-3E81-2 %U http://arxiv.org/abs/1701.02844 %D 2017 %X Choi et. al (2011) introduced a minimum spanning tree (MST)-based method called CLGrouping, for constructing tree-structured probabilistic graphical models, a statistical framework that is commonly used for inferring phylogenetic trees. While CLGrouping works correctly if there is a unique MST, we observe an indeterminacy in the method in the case that there are multiple MSTs. In this work we remove this indeterminacy by introducing so-called vertex-ranked MSTs. We note that the effectiveness of CLGrouping is inversely related to the number of leaves in the MST. This motivates the problem of finding a vertex-ranked MST with the minimum number of leaves (MLVRMST). We provide a polynomial time algorithm for the MLVRMST problem, and prove its correctness for graphs whose edges are weighted with tree-additive distances. %K Mathematics, Combinatorics, math.CO,Computer Science, Data Structures and Algorithms, cs.DS
21. Kehl T, Schneider L, Schmidt F, Stöckel D, Gerstner N, Backes C, Meese E, Keller A, Schulz MH, Lenhof H-P: RegulatorTrail: A Web Service for the Identification of Key Transcriptional Regulators. Nucleic Acids Research 2017, 45.
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@article{Kehl2017, TITLE = {{RegulatorTrail}: {A} Web Service for the Identification of Key Transcriptional Regulators}, AUTHOR = {Kehl, Tim and Schneider, Lara and Schmidt, Florian and St{\"o}ckel, Daniel and Gerstner, Nico and Backes, Christina and Meese, Eckart and Keller, Andreas and Schulz, Marcel Holger and Lenhof, Hans-Peter}, LANGUAGE = {eng}, ISSN = {0305-1048}, DOI = {10.1093/nar/gkx350}, PUBLISHER = {Oxford University Press}, ADDRESS = {Oxford}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, JOURNAL = {Nucleic Acids Research}, VOLUME = {45}, NUMBER = {W1}, PAGES = {W146--W153}, }
Endnote
%0 Journal Article %A Kehl, Tim %A Schneider, Lara %A Schmidt, Florian %A St&#246;ckel, Daniel %A Gerstner, Nico %A Backes, Christina %A Meese, Eckart %A Keller, Andreas %A Schulz, Marcel Holger %A Lenhof, Hans-Peter %+ External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations %T RegulatorTrail: A Web Service for the Identification of Key Transcriptional Regulators : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-C393-D %R 10.1093/nar/gkx350 %2 PMC5570139 %7 2017 %D 2017 %J Nucleic Acids Research %O Nucleic Acids Res %V 45 %N W1 %& W146 %P W146 - W153 %I Oxford University Press %C Oxford %@ false
22. Knops E, Schübel N, Heger E, Neumann-Fraune M, Kaiser R, Inden S, Kalaghatgi P, Sierra S: HCV Resistance Profile Evolution in a GT1b, DAA-Naive Patient Before, On, and After Failing Triple DAA Therapy. Clinical Gastroenterology and Hepatology 2017, 15.
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@article{Knops2017, TITLE = {{HCV} Resistance Profile Evolution in a {GT1b}, {DAA}-Naive Patient Before, On, and After Failing Triple {DAA} Therapy}, AUTHOR = {Knops, Elena and Sch{\"u}bel, Niels and Heger, Eva and Neumann-Fraune, Maria and Kaiser, Rolf and Inden, Stephanie and Kalaghatgi, Prabhav and Sierra, Saleta}, LANGUAGE = {eng}, DOI = {10.1016/j.cgh.2016.09.139}, PUBLISHER = {Elsevier}, ADDRESS = {Amsterdam}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, JOURNAL = {Clinical Gastroenterology and Hepatology}, VOLUME = {15}, NUMBER = {2}, PAGES = {307--309}, }
Endnote
%0 Journal Article %A Knops, Elena %A Sch&#252;bel, Niels %A Heger, Eva %A Neumann-Fraune, Maria %A Kaiser, Rolf %A Inden, Stephanie %A Kalaghatgi, Prabhav %A Sierra, Saleta %+ External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations %T HCV Resistance Profile Evolution in a GT1b, DAA-Naive Patient Before, On, and After Failing Triple DAA Therapy : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-ECB2-B %R 10.1016/j.cgh.2016.09.139 %7 2017 %D 2017 %J Clinical Gastroenterology and Hepatology %V 15 %N 2 %& 307 %P 307 - 309 %I Elsevier %C Amsterdam
23. Licciardello MP, Ringler A, Markt P, Klepsch F, Lardeau C-H, Sdelci S, Schirghuber E, Müller AC, Caldera M, Wagner A, Herzog R, Penz T, Schuster M, Boidol B, Dürnberger G, Folkvaljon Y, Stattin P, Ivanov V, Colinge J, Bock C, Kratochwill K, Menche J, Bennett KL, Kubicek S: A Combinatorial Screen of the CLOUD Uncovers a Synergy Targeting the Androgen Receptor. Nature Chemical Biology 2017, 13.
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@article{Licciardello2017, TITLE = {A Combinatorial Screen of the {CLOUD} Uncovers a Synergy Targeting the Androgen Receptor}, AUTHOR = {Licciardello, Marco P. and Ringler, Anna and Markt, Patrick and Klepsch, Freya and Lardeau, Charles-Hugues and Sdelci, Sara and Schirghuber, Erika and M{\"u}ller, Andr{\'e} C. and Caldera, Michael and Wagner, Anja and Herzog, Rebecca and Penz, Thomas and Schuster, Michael and Boidol, Bernd and D{\"u}rnberger, Gerhard and Folkvaljon, Yasin and Stattin, P{\"a}r and Ivanov, Vladimir and Colinge, Jacques and Bock, Christoph and Kratochwill, Klaus and Menche, J{\"o}rg and Bennett, Keiryn L. and Kubicek, Stefan}, LANGUAGE = {eng}, ISSN = {1552-4450}, DOI = {10.1038/nchembio.2382}, PUBLISHER = {Nature Pub. Group}, ADDRESS = {New York, NY}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, JOURNAL = {Nature Chemical Biology}, VOLUME = {13}, PAGES = {771--778}, }
Endnote
%0 Journal Article %A Licciardello, Marco P. %A Ringler, Anna %A Markt, Patrick %A Klepsch, Freya %A Lardeau, Charles-Hugues %A Sdelci, Sara %A Schirghuber, Erika %A M&#252;ller, Andr&#233; C. %A Caldera, Michael %A Wagner, Anja %A Herzog, Rebecca %A Penz, Thomas %A Schuster, Michael %A Boidol, Bernd %A D&#252;rnberger, Gerhard %A Folkvaljon, Yasin %A Stattin, P&#228;r %A Ivanov, Vladimir %A Colinge, Jacques %A Bock, Christoph %A Kratochwill, Klaus %A Menche, J&#246;rg %A Bennett, Keiryn L. %A Kubicek, Stefan %+ External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations %T A Combinatorial Screen of the CLOUD Uncovers a Synergy Targeting the Androgen Receptor : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-8F17-5 %R 10.1038/nchembio.2382 %7 2017-05-22 %D 2017 %J Nature Chemical Biology %O Nat. Chem. Biol. %V 13 %& 771 %P 771 - 778 %I Nature Pub. Group %C New York, NY %@ false
24. Li J, Casteels T, Frogne T, Ingvorsen C, Honoré C, Courtney M, Huber KVM, Schmitner N, Kimmel RA, Romanov RA, Sturtzel C, Lardeau C-H, Klughammer J, Farlik M, Sdelci S, Vieira A, Avolio F, Briand F, Baburin I, Májek P, Pauler FM, Penz T, Stukalov A, Gridling M, Parapatics K, Barbieux C, Berishvili E, Spittler A, Colinge J, Bennett KL, et al.: Artemisinins Target GABA A Receptor Signaling and Impair α Cell Identity. Cell 2017, 168.
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@article{Li2017, TITLE = {Artemisinins Target {GABA$_{\mathrm A}$} Receptor Signaling and Impair $\alpha$ Cell Identity}, AUTHOR = {Li, Jin and Casteels, Tamara and Frogne, Thomas and Ingvorsen, Camilla and Honor{\'e}, Christian and Courtney, Monica and Huber, Kilian V. M. and Schmitner, Nicole and Kimmel, Robin A. and Romanov, Roman A. and Sturtzel, Caterina and Lardeau, Charles-Hugues and Klughammer, Johanna and Farlik, Matthias and Sdelci, Sara and Vieira, Andhira and Avolio, Fabio and Briand, Fran{\c c}ois and Baburin, Igor and M{\'a}jek, Peter and Pauler, Florian M. and Penz, Thomas and Stukalov, Alexey and Gridling, Manuela and Parapatics, Katja and Barbieux, Charlotte and Berishvili, Ekaterine and Spittler, Andreas and Colinge, Jacques and Bennett, Keiryn L. and Hering, Steffen and Sulpice, Thierry and Bock, Christoph and Distel, Martin and Harkany, Tibor and Meyer, Dirk and Superti-Furga, Giulio and Collombat, Patrick and Hecksher-S{\o}rensen, Jacob and Kubicek, Stefan}, LANGUAGE = {eng}, ISSN = {0092-8674}, DOI = {10.1016/j.cell.2016.11.010}, PUBLISHER = {Elsevier}, ADDRESS = {Amsterdam}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, JOURNAL = {Cell}, VOLUME = {168}, NUMBER = {1-2}, PAGES = {86--100}, EID = {e15}, }
Endnote
%0 Journal Article %A Li, Jin %A Casteels, Tamara %A Frogne, Thomas %A Ingvorsen, Camilla %A Honor&#233;, Christian %A Courtney, Monica %A Huber, Kilian V. M. %A Schmitner, Nicole %A Kimmel, Robin A. %A Romanov, Roman A. %A Sturtzel, Caterina %A Lardeau, Charles-Hugues %A Klughammer, Johanna %A Farlik, Matthias %A Sdelci, Sara %A Vieira, Andhira %A Avolio, Fabio %A Briand, Fran&#231;ois %A Baburin, Igor %A M&#225;jek, Peter %A Pauler, Florian M. %A Penz, Thomas %A Stukalov, Alexey %A Gridling, Manuela %A Parapatics, Katja %A Barbieux, Charlotte %A Berishvili, Ekaterine %A Spittler, Andreas %A Colinge, Jacques %A Bennett, Keiryn L. %A Hering, Steffen %A Sulpice, Thierry %A Bock, Christoph %A Distel, Martin %A Harkany, Tibor %A Meyer, Dirk %A Superti-Furga, Giulio %A Collombat, Patrick %A Hecksher-S&#248;rensen, Jacob %A Kubicek, Stefan %+ External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations %T Artemisinins Target GABA A Receptor Signaling and Impair &#945; Cell Identity : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-5C72-1 %2 PMC5236063 %R 10.1016/j.cell.2016.11.010 %7 2017 %D 2017 %J Cell %V 168 %N 1-2 %& 86 %P 86 - 100 %Z sequence number: e15 %I Elsevier %C Amsterdam %@ false
25. List M, Elnegaard MP, Schmidt S, Christiansen H, Tan Q, Mollenhauer J, Baumbach J: Efficient Management of High-Throughput Screening Libraries with SAVANAH. Journal of Biomolecular Screening 2017, 22.
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@article{List2016b, TITLE = {Efficient Management of High-Throughput Screening Libraries with {SAVANAH}}, AUTHOR = {List, Markus and Elnegaard, Marlene Pedersen and Schmidt, Steffen and Christiansen, Helle and Tan, Qihua and Mollenhauer, Jan and Baumbach, Jan}, LANGUAGE = {eng}, ISSN = {1087-0571}, DOI = {10.1177/1087057116673607}, PUBLISHER = {Sage Publications, Inc.}, ADDRESS = {Larchmont, NY}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, JOURNAL = {Journal of Biomolecular Screening}, VOLUME = {22}, NUMBER = {2}, PAGES = {196--202}, }
Endnote
%0 Journal Article %A List, Markus %A Elnegaard, Marlene Pedersen %A Schmidt, Steffen %A Christiansen, Helle %A Tan, Qihua %A Mollenhauer, Jan %A Baumbach, Jan %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Efficient Management of High-Throughput Screening Libraries with SAVANAH : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-202F-D %R 10.1177/1087057116673607 %7 2016-10-11 %D 2017 %J Journal of Biomolecular Screening %V 22 %N 2 %& 196 %P 196 - 202 %I Sage Publications, Inc. %C Larchmont, NY %@ false
26. List M: Using Docker Compose for the Simple Deployment of an Integrated Drug Target Screening Platform. Journal of Integrative Bioinformatics 2017, 14.
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@article{ListJIB2017, TITLE = {Using {Docker} Compose for the Simple Deployment of an Integrated Drug Target Screening Platform}, AUTHOR = {List, Markus}, LANGUAGE = {eng}, ISSN = {1613-4516}, DOI = {10.1515/jib-2017-0016}, PUBLISHER = {de Gruyter}, ADDRESS = {Berlin}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, JOURNAL = {Journal of Integrative Bioinformatics}, VOLUME = {14}, NUMBER = {2}, }
Endnote
%0 Journal Article %A List, Markus %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Using Docker Compose for the Simple Deployment of an Integrated Drug Target Screening Platform : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-DDE3-6 %R 10.1515/jib-2017-0016 %7 2017 %D 2017 %J Journal of Integrative Bioinformatics %V 14 %N 2 %I de Gruyter %C Berlin %@ false
27. List M, Ebert P, Albrecht F: Ten Simple Rules for Developing Usable Software in Computational Biology. PLoS Computational Biology 2017, 13.
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@article{ListPloSCompBiol2016, TITLE = {Ten Simple Rules for Developing Usable Software in Computational Biology}, AUTHOR = {List, Markus and Ebert, Peter and Albrecht, Felipe}, LANGUAGE = {eng}, ISSN = {1553-734X}, DOI = {10.1371/journal.pcbi.1005265}, PUBLISHER = {Public Library of Science}, ADDRESS = {San Francisco, CA}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, JOURNAL = {PLoS Computational Biology}, VOLUME = {13}, NUMBER = {1}, EID = {e1005265}, }
Endnote
%0 Journal Article %A List, Markus %A Ebert, Peter %A Albrecht, Felipe %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Ten Simple Rules for Developing Usable Software in Computational Biology : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-203B-2 %R 10.1371/journal.pcbi.1005265 %7 2017-01-05 %D 2017 %8 05.01.2017 %J PLoS Computational Biology %V 13 %N 1 %Z sequence number: e1005265 %I Public Library of Science %C San Francisco, CA %@ false
28. Liu W, Tsang IW, Müller K-R: An Easy-to-hard Learning Paradigm for Multiple Classes and Multiple Labels. The Journal of Machine Learning Research 2017, 18.
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@article{Liu2017, TITLE = {An Easy-to-hard Learning Paradigm for Multiple Classes and Multiple Labels}, AUTHOR = {Liu, Weiwei and Tsang, Ivor W. and M{\"u}ller, Klaus-Robert}, LANGUAGE = {eng}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, JOURNAL = {The Journal of Machine Learning Research}, VOLUME = {18}, }
Endnote
%0 Journal Article %A Liu, Weiwei %A Tsang, Ivor W. %A M&#252;ller, Klaus-Robert %+ External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T An Easy-to-hard Learning Paradigm for Multiple Classes and Multiple Labels : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-1781-C %7 2017 %D 2017 %J The Journal of Machine Learning Research %V 18
29. Lund JB, List M, Baumbach J: Interactive Microbial Distribution Analysis using BioAtlas. Nucleic Acids Research 2017, 45.
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@article{LundListBaumbach2017, TITLE = {Interactive microbial distribution analysis using {BioAtlas}}, AUTHOR = {Lund, Jesper Beltoft and List, Markus and Baumbach, Jan}, LANGUAGE = {eng}, ISSN = {0305-1048}, DOI = {10.1093/nar/gkx304}, PUBLISHER = {Oxford University Press}, ADDRESS = {Oxford}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, JOURNAL = {Nucleic Acids Research}, VOLUME = {45}, NUMBER = {W1}, PAGES = {W509--W513}, }
Endnote
%0 Journal Article %A Lund, Jesper Beltoft %A List, Markus %A Baumbach, Jan %+ External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Interactive Microbial Distribution Analysis using BioAtlas : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-C387-9 %R 10.1093/nar/gkx304 %2 PMC5570126 %7 2017 %D 2017 %J Nucleic Acids Research %O Nucleic Acids Res %V 45 %N W1 %& W509 %P W509 - W513 %I Oxford University Press %C Oxford %@ false
30. Müller F: Analyzing DNA Methylation Signatures of Cell Identity. Universität des Saarlandes; 2017.
Abstract
Although virtually all cells in an organism share the same genome, regulatory mechanisms give rise to hundreds of different, highly specialized cell types. Understanding these mechanisms has been in the limelight of epigenomic research. It is now evident that cellular identity is inscribed in the epigenome of each individual cell. Nonetheless, the precise mechanisms by which different epigenomic marks are involved in regulating gene expression are just beginning to be unraveled. Furthermore, epigenomic patterns are highly dynamic and subject to environmental influences. Any given cell type is defined by cell populations exhibiting epigenetic heterogeneity at different levels. Characterizing this heterogeneity is paramount in understanding the regulatory role of the epigenome. Different epigenomic marks can be profiled using high-throughput sequencing, and global initiatives have started to provide a comprehensive picture of the human epigenome by assaying a multitude of marks across a broad panel of cell types and conditions. In particular, DNA methylation has been extensively studied for its gene-regulatory role in health and disease. This thesis describes computational methods and pipelines for the analysis of DNA methylation data. It provides concepts for addressing bioinformatic challenges such as the processing of large, epigenome-wide datasets and integrating multiple levels of information in an interpretable manner. We developed RnBeads, an R package that facilitates comprehensive, interpretable analysis of large-scale DNA methylation datasets at the level of single CpGs or genomic regions of interest. With the epiRepeatR pipeline, we introduced additional tools for studying global patterns of epigenomic marks in transposons and other repetitive regions of the genome. Blood-cell differentiation represents a useful model for studying trajectories of cellular differentiation. We developed and applied bioinformatic methods to dissect the DNA methylation landscape of the hematopoietic system. Here, we provide a broad outline of cell-type-specific DNA methylation signatures and phenotypic diversity reflected in the epigenomes of human mature blood cells. We also describe the DNA methylation dynamics in the process of immune memory formation in T helper cells. Moreover, we portrayed epigenetic fingerprints of defined progenitor cell types and derived computational models that were capable of accurately inferring cell identity. We used these models in order to characterize heterogeneity in progenitor cell populations, to identify DNA methylation signatures of hematopoietic differentiation and to infer the epigenomic similarities of blood cell types. Finally, by interpreting DNA methylation patterns in leukemia and derived pluripotent cells, we started to discern how epigenomic patterns are altered in disease and explored how reprogramming of these patterns could potentially be used to restore a non-malignant state. In summary, this work showcases novel methods and computational tools for the identification and interpretation of epigenetic signatures of cell identity. It provides a detailed view on the epigenomic landscape spanned by DNA methylation patterns in hematopoietic cells that enhances our understanding of epigenetic regulation in cell differentiation and disease.
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@phdthesis{muellerphd17, TITLE = {Analyzing {DNA} Methylation Signatures of Cell Identity}, AUTHOR = {M{\"u}ller, Fabian}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291-scidok-69432}, DOI = {10.17617/2.2474737}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, ABSTRACT = {Although virtually all cells in an organism share the same genome, regulatory mechanisms give rise to hundreds of different, highly specialized cell types. Understanding these mechanisms has been in the limelight of epigenomic research. It is now evident that cellular identity is inscribed in the epigenome of each individual cell. Nonetheless, the precise mechanisms by which different epigenomic marks are involved in regulating gene expression are just beginning to be unraveled. Furthermore, epigenomic patterns are highly dynamic and subject to environmental influences. Any given cell type is defined by cell populations exhibiting epigenetic heterogeneity at different levels. Characterizing this heterogeneity is paramount in understanding the regulatory role of the epigenome. Different epigenomic marks can be profiled using high-throughput sequencing, and global initiatives have started to provide a comprehensive picture of the human epigenome by assaying a multitude of marks across a broad panel of cell types and conditions. In particular, DNA methylation has been extensively studied for its gene-regulatory role in health and disease. This thesis describes computational methods and pipelines for the analysis of DNA methylation data. It provides concepts for addressing bioinformatic challenges such as the processing of large, epigenome-wide datasets and integrating multiple levels of information in an interpretable manner. We developed RnBeads, an R package that facilitates comprehensive, interpretable analysis of large-scale DNA methylation datasets at the level of single CpGs or genomic regions of interest. With the epiRepeatR pipeline, we introduced additional tools for studying global patterns of epigenomic marks in transposons and other repetitive regions of the genome. Blood-cell differentiation represents a useful model for studying trajectories of cellular differentiation. We developed and applied bioinformatic methods to dissect the DNA methylation landscape of the hematopoietic system. Here, we provide a broad outline of cell-type-specific DNA methylation signatures and phenotypic diversity reflected in the epigenomes of human mature blood cells. We also describe the DNA methylation dynamics in the process of immune memory formation in T helper cells. Moreover, we portrayed epigenetic fingerprints of defined progenitor cell types and derived computational models that were capable of accurately inferring cell identity. We used these models in order to characterize heterogeneity in progenitor cell populations, to identify DNA methylation signatures of hematopoietic differentiation and to infer the epigenomic similarities of blood cell types. Finally, by interpreting DNA methylation patterns in leukemia and derived pluripotent cells, we started to discern how epigenomic patterns are altered in disease and explored how reprogramming of these patterns could potentially be used to restore a non-malignant state. In summary, this work showcases novel methods and computational tools for the identification and interpretation of epigenetic signatures of cell identity. It provides a detailed view on the epigenomic landscape spanned by DNA methylation patterns in hematopoietic cells that enhances our understanding of epigenetic regulation in cell differentiation and disease.}, }
Endnote
%0 Thesis %A M&#252;ller, Fabian %Y Lengauer, Thomas %A referee: Bock, Christoph %A referee: Brors, Benedikt %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations %T Analyzing DNA Methylation Signatures of Cell Identity : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-D9AA-6 %U urn:nbn:de:bsz:291-scidok-69432 %R 10.17617/2.2474737 %I Universit&#228;t des Saarlandes %C Saarbr&#252;cken %D 2017 %P 177 p. %V phd %9 phd %X Although virtually all cells in an organism share the same genome, regulatory mechanisms give rise to hundreds of different, highly specialized cell types. Understanding these mechanisms has been in the limelight of epigenomic research. It is now evident that cellular identity is inscribed in the epigenome of each individual cell. Nonetheless, the precise mechanisms by which different epigenomic marks are involved in regulating gene expression are just beginning to be unraveled. Furthermore, epigenomic patterns are highly dynamic and subject to environmental influences. Any given cell type is defined by cell populations exhibiting epigenetic heterogeneity at different levels. Characterizing this heterogeneity is paramount in understanding the regulatory role of the epigenome. Different epigenomic marks can be profiled using high-throughput sequencing, and global initiatives have started to provide a comprehensive picture of the human epigenome by assaying a multitude of marks across a broad panel of cell types and conditions. In particular, DNA methylation has been extensively studied for its gene-regulatory role in health and disease. This thesis describes computational methods and pipelines for the analysis of DNA methylation data. It provides concepts for addressing bioinformatic challenges such as the processing of large, epigenome-wide datasets and integrating multiple levels of information in an interpretable manner. We developed RnBeads, an R package that facilitates comprehensive, interpretable analysis of large-scale DNA methylation datasets at the level of single CpGs or genomic regions of interest. With the epiRepeatR pipeline, we introduced additional tools for studying global patterns of epigenomic marks in transposons and other repetitive regions of the genome. Blood-cell differentiation represents a useful model for studying trajectories of cellular differentiation. We developed and applied bioinformatic methods to dissect the DNA methylation landscape of the hematopoietic system. Here, we provide a broad outline of cell-type-specific DNA methylation signatures and phenotypic diversity reflected in the epigenomes of human mature blood cells. We also describe the DNA methylation dynamics in the process of immune memory formation in T helper cells. Moreover, we portrayed epigenetic fingerprints of defined progenitor cell types and derived computational models that were capable of accurately inferring cell identity. We used these models in order to characterize heterogeneity in progenitor cell populations, to identify DNA methylation signatures of hematopoietic differentiation and to infer the epigenomic similarities of blood cell types. Finally, by interpreting DNA methylation patterns in leukemia and derived pluripotent cells, we started to discern how epigenomic patterns are altered in disease and explored how reprogramming of these patterns could potentially be used to restore a non-malignant state. In summary, this work showcases novel methods and computational tools for the identification and interpretation of epigenetic signatures of cell identity. It provides a detailed view on the epigenomic landscape spanned by DNA methylation patterns in hematopoietic cells that enhances our understanding of epigenetic regulation in cell differentiation and disease. %U http://scidok.sulb.uni-saarland.de/volltexte/2017/6943/http://scidok.sulb.uni-saarland.de/doku/lic_ohne_pod.php?la=de
31. Müller H, Jimenez-Heredia R, Krolo A, Hirschmugl T, Dmytrus J, Boztug K, Bock C: VCF.Filter: Interactive Prioritization of Disease-linked Genetic Variants from Sequencing Data. Nucleic Acids Research 2017, 45.
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@article{BockVCF2017, TITLE = {{VCF}.{Filter}: {I}nteractive Prioritization of Disease-linked Genetic Variants from Sequencing Data}, AUTHOR = {M{\"u}ller, Heiko and Jimenez-Heredia, Raul and Krolo, Ana and Hirschmugl, Tatjana and Dmytrus, Jasmin and Boztug, Kaan and Bock, Christoph}, LANGUAGE = {eng}, ISSN = {0305-1048}, DOI = {10.1093/nar/gkx425}, PUBLISHER = {Oxford University Press}, ADDRESS = {Oxford}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, JOURNAL = {Nucleic Acids Research}, VOLUME = {45}, NUMBER = {W1}, PAGES = {W567--W572}, }
Endnote
%0 Journal Article %A M&#252;ller, Heiko %A Jimenez-Heredia, Raul %A Krolo, Ana %A Hirschmugl, Tatjana %A Dmytrus, Jasmin %A Boztug, Kaan %A Bock, Christoph %+ External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T VCF.Filter: Interactive Prioritization of Disease-linked Genetic Variants from Sequencing Data : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-C37D-1 %R 10.1093/nar/gkx425 %2 PMC5570181 %7 2017 %D 2017 %J Nucleic Acids Research %O Nucleic Acids Res %V 45 %N W1 %& W567 %P W567 - W572 %I Oxford University Press %C Oxford %@ false
32. Neogi U, Siddik AB, Kalaghatgi P, Gisslén M, Bratt G, Marrone G, Sönnerborg A: Recent Increased Identification and Transmission of HIV-1 unique Recombinant Forms in Sweden. Scientific Reports 2017, 7.
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@article{Neogi2017, TITLE = {Recent Increased Identification and Transmission of {HIV}-1 unique Recombinant Forms in {Sweden}}, AUTHOR = {Neogi, Ujjwal and Siddik, Abu Bakar and Kalaghatgi, Prabhav and Gissl{\'e}n, Magnus and Bratt, G{\"o}ran and Marrone, Gaetano and S{\"o}nnerborg, Anders}, LANGUAGE = {eng}, ISSN = {2045-2322}, DOI = {10.1038/s41598-017-06860-2}, PUBLISHER = {Nature Publishing Group}, ADDRESS = {London, UK}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, JOURNAL = {Scientific Reports}, VOLUME = {7}, EID = {6371}, }
Endnote
%0 Journal Article %A Neogi, Ujjwal %A Siddik, Abu Bakar %A Kalaghatgi, Prabhav %A Gissl&#233;n, Magnus %A Bratt, G&#246;ran %A Marrone, Gaetano %A S&#246;nnerborg, Anders %+ External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations %T Recent Increased Identification and Transmission of HIV-1 unique Recombinant Forms in Sweden : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-BC30-F %2 PMC5527090 %R 10.1038/s41598-017-06860-2 %7 2017 %D 2017 %J Scientific Reports %O Sci. Rep. %V 7 %Z sequence number: 6371 %I Nature Publishing Group %C London, UK %@ false
33. Nikumbh S, Pfeifer N: Genetic Sequence-based Prediction of Long-range Chromatin Interactions Suggests a Potential Role of Short Tandem Repeat Sequences in Genome Organization. BMC Bioinformatics 2017, 18.
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@article{Nikumbh2017, TITLE = {Genetic Sequence-based Prediction of Long-range Chromatin Interactions Suggests a Potential Role of Short Tandem Repeat Sequences in Genome Organization}, AUTHOR = {Nikumbh, Sarvesh and Pfeifer, Nico}, LANGUAGE = {eng}, ISSN = {1471-2105}, DOI = {10.1186/s12859-017-1624-x}, PUBLISHER = {BioMed Central}, ADDRESS = {London}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, JOURNAL = {BMC Bioinformatics}, VOLUME = {18}, PAGES = {1--16}, EID = {218}, }
Endnote
%0 Journal Article %A Nikumbh, Sarvesh %A Pfeifer, Nico %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Genetic Sequence-based Prediction of Long-range Chromatin Interactions Suggests a Potential Role of Short Tandem Repeat Sequences in Genome Organization : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-26AD-4 %R 10.1186/s12859-017-1624-x %7 2017-04-18 %D 2017 %8 18.04.2017 %J BMC Bioinformatics %V 18 %& 1 %P 1 - 16 %Z sequence number: 218 %I BioMed Central %C London %@ false
34. Nikumbh S, Ebert P, Pfeifer N: All Fingers Are Not the Same: Handling Variable-Length Sequences in a Discriminative Setting Using Conformal Multi-Instance Kernels. In 17th International Workshop on Algorithms in Bioinformatics (WABI 2017). Schloss Dagstuhl; 2017. [Leibniz International Proceedings in Informatics, vol. 88]
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@inproceedings{nikumbh_et_al:LIPIcs:2017:7645, TITLE = {All Fingers Are Not the Same: {H}andling Variable-Length Sequences in a Discriminative Setting Using Conformal Multi-Instance Kernels}, AUTHOR = {Nikumbh, Sarvesh and Ebert, Peter and Pfeifer, Nico}, LANGUAGE = {eng}, ISSN = {1868-8969}, ISBN = {978-3-95977-050-7}, DOI = {10.4230/LIPIcs.WABI.2017.16}, PUBLISHER = {Schloss Dagstuhl}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {17th International Workshop on Algorithms in Bioinformatics (WABI 2017)}, EDITOR = {Schwartz, Russell and Reinert, Knut}, PAGES = {1--14}, EID = {16}, SERIES = {Leibniz International Proceedings in Informatics}, VOLUME = {88}, ADDRESS = {Boston, MA, USA}, }
Endnote
%0 Conference Proceedings %A Nikumbh, Sarvesh %A Ebert, Peter %A Pfeifer, Nico %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T All Fingers Are Not the Same: Handling Variable-Length Sequences in a Discriminative Setting Using Conformal Multi-Instance Kernels : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-BC17-7 %R 10.4230/LIPIcs.WABI.2017.16 %D 2017 %B 17th International Workshop on Algorithms in Bioinformatics %Z date of event: 2017-08-21 - 2017-08-23 %C Boston, MA, USA %B 17th International Workshop on Algorithms in Bioinformatics %E Schwartz, Russell; Reinert, Knut %P 1 - 14 %Z sequence number: 16 %I Schloss Dagstuhl %@ 978-3-95977-050-7 %B Leibniz International Proceedings in Informatics %N 88 %@ false %U http://drops.dagstuhl.de/opus/volltexte/2017/7645/http://drops.dagstuhl.de/doku/urheberrecht1.html
35. Pironti A, Pfeifer N, Walter H, Jensen B-EO, Zazzi M, Gomes P, Kaiser R, Lengauer T: Using Drug Exposure for Predicting Drug Resistance – A data-driven Genotypic Interpretation Tool. PLoS One 2017, 12.
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@article{Pironti_Pfeifer_Lengauer_2017, TITLE = {Using Drug Exposure for Predicting Drug Resistance -- A data-driven Genotypic Interpretation Tool}, AUTHOR = {Pironti, Alejandro and Pfeifer, Nico and Walter, Hauke and Jensen, Bj{\"o}rn-Erik O. and Zazzi, Maurizio and Gomes, Perp{\'e}tua and Kaiser, Rolf and Lengauer, Thomas}, LANGUAGE = {eng}, ISSN = {1932-6203}, DOI = {10.1371/journal.pone.0174992}, PUBLISHER = {Public Library of Science}, ADDRESS = {San Francisco, CA}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, JOURNAL = {PLoS One}, VOLUME = {12}, NUMBER = {4}, EID = {e0174992}, }
Endnote
%0 Journal Article %A Pironti, Alejandro %A Pfeifer, Nico %A Walter, Hauke %A Jensen, Bj&#246;rn-Erik O. %A Zazzi, Maurizio %A Gomes, Perp&#233;tua %A Kaiser, Rolf %A Lengauer, Thomas %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Using Drug Exposure for Predicting Drug Resistance &#8211; A data-driven Genotypic Interpretation Tool : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-48E5-0 %R 10.1371/journal.pone.0174992 %2 PMC5386274 %7 2017-04-10 %D 2017 %8 10.04.2017 %J PLoS One %V 12 %N 4 %Z sequence number: e0174992 %I Public Library of Science %C San Francisco, CA %@ false
36. Pironti A, Walter H, Pfeifer N, Knops E, Lübke N, Büch J, Di Giambenedetto S, Kaiser R, Lengauer T: Determination of Phenotypic Resistance Cutoffs from Routine Clinical Data. Journal of Acquired Immune Deficiency Syndromes 2017, 74.
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@article{Pironti_Pfeifer_Lengauer2017, TITLE = {Determination of Phenotypic Resistance Cutoffs from Routine Clinical Data}, AUTHOR = {Pironti, Alejandro and Walter, Hauke and Pfeifer, Nico and Knops, Elena and L{\"u}bke, Nadine and B{\"u}ch, Joachim and Di Giambenedetto, Simona and Kaiser, Rolf and Lengauer, Thomas}, LANGUAGE = {eng}, DOI = {10.1097/QAI.0000000000001198}, PUBLISHER = {Lippincott Williams \& Wilkins}, ADDRESS = {Philadelphia, PA}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, JOURNAL = {Journal of Acquired Immune Deficiency Syndromes}, VOLUME = {74}, NUMBER = {5}, PAGES = {e129--e137}, }
Endnote
%0 Journal Article %A Pironti, Alejandro %A Walter, Hauke %A Pfeifer, Nico %A Knops, Elena %A L&#252;bke, Nadine %A B&#252;ch, Joachim %A Di Giambenedetto, Simona %A Kaiser, Rolf %A Lengauer, Thomas %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Determination of Phenotypic Resistance Cutoffs from Routine Clinical Data : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-3F1D-D %R 10.1097/QAI.0000000000001198 %7 2017 %D 2017 %J Journal of Acquired Immune Deficiency Syndromes %O JAIDS %V 74 %N 5 %& e129 %P e129 - e137 %I Lippincott Williams & Wilkins %C Philadelphia, PA
37. Porubsky D, Garg S, Sanders AD, Korbel JO, Guryev V, Lansdorp PM, Marschall T: Dense and Accurate Whole-chromosome Haplotyping of Individual Genomes. Nature Communications 2017, 8.
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@article{Porubsky2017, TITLE = {Dense and Accurate Whole-chromosome Haplotyping of Individual Genomes}, AUTHOR = {Porubsky, David and Garg, Shilpa and Sanders, Ashley D. and Korbel, Jan O. and Guryev, Victor and Lansdorp, Peter M. and Marschall, Tobias}, LANGUAGE = {eng}, ISSN = {2041-1723}, DOI = {10.1038/s41467-017-01389-4}, PUBLISHER = {Nature Publishing Group}, ADDRESS = {London}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, JOURNAL = {Nature Communications}, VOLUME = {8}, EID = {1293}, }
Endnote
%0 Journal Article %A Porubsky, David %A Garg, Shilpa %A Sanders, Ashley D. %A Korbel, Jan O. %A Guryev, Victor %A Lansdorp, Peter M. %A Marschall, Tobias %+ External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Dense and Accurate Whole-chromosome Haplotyping of Individual Genomes : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-3109-E %R 10.1038/s41467-017-01389-4 %7 2017 %D 2017 %J Nature Communications %O Nat. Commun. %V 8 %Z sequence number: 1293 %I Nature Publishing Group %C London %@ false
38. Schmidt F, Gasparoni N, Gasparoni G, Gianmoena K, Cadenas C, Polansky JK, Ebert P, Nordström K, Barann M, Sinha A, Fröhler S, Xiong J, Dheghani Amirabad A, Behjati Ardakani F, Hutter B, Zipprich G, Felder B, Eils J, Brors B, Chen W, Hengstler JG, Hamann A, Lengauer T, Rosenstiel P, Walter J, Schulz MH: Combining Transcription Factor Binding Affinities with Open-Chromatin Data for Accurate Gene Expression Prediction. Nucleic Acids Research 2017, 45.
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@article{pmid27899623, TITLE = {Combining Transcription Factor Binding Affinities with Open-Chromatin Data for Accurate Gene Expression Prediction}, AUTHOR = {Schmidt, Florian and Gasparoni, Nina and Gasparoni, Gilles and Gianmoena, Kathrin and Cadenas, Cristina and Polansky, Julia K. and Ebert, Peter and Nordstr{\"o}m, Karl and Barann, Matthias and Sinha, Anupam and Fr{\"o}hler, Sebastian and Xiong, Jieyi and Dheghani Amirabad, Azim and Behjati Ardakani, Fatemeh and Hutter, Barbara and Zipprich, Gideon and Felder, B{\"a}rbel and Eils, J{\"u}rgen and Brors, Benedikt and Chen, Wei and Hengstler, Jan G. and Hamann, Alf and Lengauer, Thomas and Rosenstiel, Philip and Walter, J{\"o}rn and Schulz, Marcel H.}, LANGUAGE = {eng}, ISSN = {0305-1048}, DOI = {10.1093/nar/gkw1061}, PUBLISHER = {Oxford University Press}, ADDRESS = {Oxford}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, JOURNAL = {Nucleic Acids Research}, VOLUME = {45}, NUMBER = {1}, PAGES = {54--66}, }
Endnote
%0 Journal Article %A Schmidt, Florian %A Gasparoni, Nina %A Gasparoni, Gilles %A Gianmoena, Kathrin %A Cadenas, Cristina %A Polansky, Julia K. %A Ebert, Peter %A Nordstr&#246;m, Karl %A Barann, Matthias %A Sinha, Anupam %A Fr&#246;hler, Sebastian %A Xiong, Jieyi %A Dheghani Amirabad, Azim %A Behjati Ardakani, Fatemeh %A Hutter, Barbara %A Zipprich, Gideon %A Felder, B&#228;rbel %A Eils, J&#252;rgen %A Brors, Benedikt %A Chen, Wei %A Hengstler, Jan G. %A Hamann, Alf %A Lengauer, Thomas %A Rosenstiel, Philip %A Walter, J&#246;rn %A Schulz, Marcel H. %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Combining Transcription Factor Binding Affinities with Open-Chromatin Data for Accurate Gene Expression Prediction : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-378F-F %R 10.1093/nar/gkw1061 %7 2016 %D 2017 %J Nucleic Acids Research %O Nucleic Acids Res %V 45 %N 1 %& 54 %P 54 - 66 %I Oxford University Press %C Oxford %@ false
39. Sheffield NC, Pierron G, Klughammer J, Datlinger P, Schönegger A, Schuster M, Hadler J, Surdez D, Guillemot D, Lapouble E, Freneaux P, Champigneulle J, Bouvier R, Walder D, Ambros IM, Hutter C, Sorz E, Amaral AT, de Álava E, Schallmoser K, Strunk D, Rinner B, Liegl-Atzwanger B, Huppertz B, Leithner A, de Pinieux G, Terrier P, Laurence V, Michon J, Ladenstein R, et al.: DNA Methylation Heterogeneity Defines a Disease Spectrum in Ewing Sarcoma. Nature Medicine 2017, 23.
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@article{Sheffield2017, TITLE = {{DNA} methylation heterogeneity defines a disease spectrum in {Ewing} sarcoma}, AUTHOR = {Sheffield, Nathan C. and Pierron, Gaelle and Klughammer, Johanna and Datlinger, Paul and Sch{\"o}negger, Andreas and Schuster, Michael and Hadler, Johanna and Surdez, Didier and Guillemot, Delphine and Lapouble, Eve and Freneaux, Paul and Champigneulle, Jacqueline and Bouvier, Raymonde and Walder, Diana and Ambros, Ingeborg M. and Hutter, Caroline and Sorz, Eva and Amaral, Ana T. and de {\'A}lava, Enrique and Schallmoser, Katharina and Strunk, Dirk and Rinner, Beate and Liegl-Atzwanger, Bernadette and Huppertz, Berthold and Leithner, Andreas and de Pinieux, Gonzague and Terrier, Philippe and Laurence, Val{\'e}rie and Michon, Jean and Ladenstein, Ruth and Holter, Wolfgang and Windhager, Reinhard and Dirksen, Uta and Ambros, Peter F. and Delattre, Olivier and Kovar, Heinrich and Bock, Christoph and Tomazou, Eleni M.}, LANGUAGE = {eng}, ISSN = {1078-8956}, DOI = {10.1038/nm.4273}, PUBLISHER = {Nature Publishing Group}, ADDRESS = {New York, NY}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, JOURNAL = {Nature Medicine}, VOLUME = {23}, NUMBER = {3}, PAGES = {386--395}, }
Endnote
%0 Journal Article %A Sheffield, Nathan C. %A Pierron, Gaelle %A Klughammer, Johanna %A Datlinger, Paul %A Sch&#246;negger, Andreas %A Schuster, Michael %A Hadler, Johanna %A Surdez, Didier %A Guillemot, Delphine %A Lapouble, Eve %A Freneaux, Paul %A Champigneulle, Jacqueline %A Bouvier, Raymonde %A Walder, Diana %A Ambros, Ingeborg M. %A Hutter, Caroline %A Sorz, Eva %A Amaral, Ana T. %A de &#193;lava, Enrique %A Schallmoser, Katharina %A Strunk, Dirk %A Rinner, Beate %A Liegl-Atzwanger, Bernadette %A Huppertz, Berthold %A Leithner, Andreas %A de Pinieux, Gonzague %A Terrier, Philippe %A Laurence, Val&#233;rie %A Michon, Jean %A Ladenstein, Ruth %A Holter, Wolfgang %A Windhager, Reinhard %A Dirksen, Uta %A Ambros, Peter F. %A Delattre, Olivier %A Kovar, Heinrich %A Bock, Christoph %A Tomazou, Eleni M. %+ External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations %T DNA Methylation Heterogeneity Defines a Disease Spectrum in Ewing Sarcoma : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-CBE6-E %R 10.1038/nm.4273 %7 2017-01-30 %D 2017 %J Nature Medicine %O Nat. Med. %V 23 %N 3 %& 386 %P 386 - 395 %I Nature Publishing Group %C New York, NY %@ false
40. Siu A: Knowledge-driven Entity Recognition and Disambiguation in Biomedical Text. Universität des Saarlandes; 2017.
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BibTeX
@phdthesis{siuphd17, TITLE = {Knowledge-driven Entity Recognition and Disambiguation in Biomedical Text}, AUTHOR = {Siu, Amy}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, }
Endnote
%0 Thesis %A Siu, Amy %Y Weikum, Gerhard %A referee: Berberich, Klaus %A referee: Leser, Ulf %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Knowledge-driven Entity Recognition and Disambiguation in Biomedical Text : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-DD18-E %I Universit&#228;t des Saarlandes %C Saarbr&#252;cken %D 2017 %P 169 p. %V phd %9 phd
41. Towards Multiple Kernel Principal Component Analysis for Integrative Analysis of Tumor Samples [http://arxiv.org/abs/1701.00422]
(arXiv: 1701.00422)
Abstract
Personalized treatment of patients based on tissue-specific cancer subtypes has strongly increased the efficacy of the chosen therapies. Even though the amount of data measured for cancer patients has increased over the last years, most cancer subtypes are still diagnosed based on individual data sources (e.g. gene expression data). We propose an unsupervised data integration method based on kernel principal component analysis. Principal component analysis is one of the most widely used techniques in data analysis. Unfortunately, the straight-forward multiple-kernel extension of this method leads to the use of only one of the input matrices, which does not fit the goal of gaining information from all data sources. Therefore, we present a scoring function to determine the impact of each input matrix. The approach enables visualizing the integrated data and subsequent clustering for cancer subtype identification. Due to the nature of the method, no free parameters have to be set. We apply the methodology to five different cancer data sets and demonstrate its advantages in terms of results and usability.
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@online{SpeicherarXiv2017, TITLE = {Towards Multiple Kernel Principal Component Analysis for Integrative Analysis of Tumor Samples}, AUTHOR = {Speicher, Nora K. and Pfeifer, Nico}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1701.00422}, EPRINT = {1701.00422}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Personalized treatment of patients based on tissue-specific cancer subtypes has strongly increased the efficacy of the chosen therapies. Even though the amount of data measured for cancer patients has increased over the last years, most cancer subtypes are still diagnosed based on individual data sources (e.g. gene expression data). We propose an unsupervised data integration method based on kernel principal component analysis. Principal component analysis is one of the most widely used techniques in data analysis. Unfortunately, the straight-forward multiple-kernel extension of this method leads to the use of only one of the input matrices, which does not fit the goal of gaining information from all data sources. Therefore, we present a scoring function to determine the impact of each input matrix. The approach enables visualizing the integrated data and subsequent clustering for cancer subtype identification. Due to the nature of the method, no free parameters have to be set. We apply the methodology to five different cancer data sets and demonstrate its advantages in terms of results and usability.}, }
Endnote
%0 Report %A Speicher, Nora K. %A Pfeifer, Nico %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Towards Multiple Kernel Principal Component Analysis for Integrative Analysis of Tumor Samples : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-4D68-3 %U http://arxiv.org/abs/1701.00422 %D 2017 %X Personalized treatment of patients based on tissue-specific cancer subtypes has strongly increased the efficacy of the chosen therapies. Even though the amount of data measured for cancer patients has increased over the last years, most cancer subtypes are still diagnosed based on individual data sources (e.g. gene expression data). We propose an unsupervised data integration method based on kernel principal component analysis. Principal component analysis is one of the most widely used techniques in data analysis. Unfortunately, the straight-forward multiple-kernel extension of this method leads to the use of only one of the input matrices, which does not fit the goal of gaining information from all data sources. Therefore, we present a scoring function to determine the impact of each input matrix. The approach enables visualizing the integrated data and subsequent clustering for cancer subtype identification. Due to the nature of the method, no free parameters have to be set. We apply the methodology to five different cancer data sets and demonstrate its advantages in terms of results and usability. %K Statistics, Machine Learning, stat.ML
42. Sun P: Bi-(N-) cluster editing and its biomedical applications. Universität des Saarlandes; 2017.
Abstract
he extremely fast advances in wet-lab techniques lead to an exponential growth of heterogeneous and unstructured biological data, posing a great challenge to data integration in nowadays system biology. The traditional clustering approach, although widely used to divide the data into groups sharing common features, is less powerful in the analysis of heterogeneous data from n different sources (n _ 2). The co-clustering approach has been widely used for combined analyses of multiple networks to address the challenge of heterogeneity. In this thesis, novel methods for the co-clustering of large scale heterogeneous data sets are presented in the software package n-CluE: one exact algorithm and two heuristic algorithms based on the model of bi-/n-cluster editing by modeling the input as n-partite graphs and solving the clustering problem with various strategies. In the first part of the thesis, the complexity and the fixed-parameter tractability of the extended bicluster editing model with relaxed constraints are investigated, namely the ?-bicluster editing model and its NP-hardness is proven. Based on the results of this analysis, three strategies within the n-CluE software package are then established and discussed, together with the evaluations on performances and the systematic comparisons against other algorithms of the same type in solving bi-/n-cluster editing problem. To demonstrate the practical impact, three real-world analyses using n-CluE are performed, including (a) prediction of novel genotype-phenotype associations by clustering the data from Genome-Wide Association Studies; (b) comparison between n-CluE and eight other biclustering tools on GEO Omnibus microarray data sets; (c) drug repositioning predictions by co-clustering on drug, gene and disease networks. The outstanding performance of n-CluE in the real-world applications shows its strength and flexibility in integrating heterogeneous data and extracting biological relevant information in bioinformatic analyses.
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BibTeX
@phdthesis{Sunphd17, TITLE = {Bi-(N-) cluster editing and its biomedical applications}, AUTHOR = {Sun, Peng}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291-scidok-69309}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, ABSTRACT = {he extremely fast advances in wet-lab techniques lead to an exponential growth of heterogeneous and unstructured biological data, posing a great challenge to data integration in nowadays system biology. The traditional clustering approach, although widely used to divide the data into groups sharing common features, is less powerful in the analysis of heterogeneous data from n different sources (n _ 2). The co-clustering approach has been widely used for combined analyses of multiple networks to address the challenge of heterogeneity. In this thesis, novel methods for the co-clustering of large scale heterogeneous data sets are presented in the software package n-CluE: one exact algorithm and two heuristic algorithms based on the model of bi-/n-cluster editing by modeling the input as n-partite graphs and solving the clustering problem with various strategies. In the first part of the thesis, the complexity and the fixed-parameter tractability of the extended bicluster editing model with relaxed constraints are investigated, namely the ?-bicluster editing model and its NP-hardness is proven. Based on the results of this analysis, three strategies within the n-CluE software package are then established and discussed, together with the evaluations on performances and the systematic comparisons against other algorithms of the same type in solving bi-/n-cluster editing problem. To demonstrate the practical impact, three real-world analyses using n-CluE are performed, including (a) prediction of novel genotype-phenotype associations by clustering the data from Genome-Wide Association Studies; (b) comparison between n-CluE and eight other biclustering tools on GEO Omnibus microarray data sets; (c) drug repositioning predictions by co-clustering on drug, gene and disease networks. The outstanding performance of n-CluE in the real-world applications shows its strength and flexibility in integrating heterogeneous data and extracting biological relevant information in bioinformatic analyses.}, }
Endnote
%0 Thesis %A Sun, Peng %Y Baumbach, Jan %A referee: Guo, Jiong %A referee: Lengauer, Thomas %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Bi-(N-) cluster editing and its biomedical applications : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-A65E-F %U urn:nbn:de:bsz:291-scidok-69309 %I Universit&#228;t des Saarlandes %C Saarbr&#252;cken %D 2017 %P 192 p. %V phd %9 phd %X he extremely fast advances in wet-lab techniques lead to an exponential growth of heterogeneous and unstructured biological data, posing a great challenge to data integration in nowadays system biology. The traditional clustering approach, although widely used to divide the data into groups sharing common features, is less powerful in the analysis of heterogeneous data from n different sources (n _ 2). The co-clustering approach has been widely used for combined analyses of multiple networks to address the challenge of heterogeneity. In this thesis, novel methods for the co-clustering of large scale heterogeneous data sets are presented in the software package n-CluE: one exact algorithm and two heuristic algorithms based on the model of bi-/n-cluster editing by modeling the input as n-partite graphs and solving the clustering problem with various strategies. In the first part of the thesis, the complexity and the fixed-parameter tractability of the extended bicluster editing model with relaxed constraints are investigated, namely the ?-bicluster editing model and its NP-hardness is proven. Based on the results of this analysis, three strategies within the n-CluE software package are then established and discussed, together with the evaluations on performances and the systematic comparisons against other algorithms of the same type in solving bi-/n-cluster editing problem. To demonstrate the practical impact, three real-world analyses using n-CluE are performed, including (a) prediction of novel genotype-phenotype associations by clustering the data from Genome-Wide Association Studies; (b) comparison between n-CluE and eight other biclustering tools on GEO Omnibus microarray data sets; (c) drug repositioning predictions by co-clustering on drug, gene and disease networks. The outstanding performance of n-CluE in the real-world applications shows its strength and flexibility in integrating heterogeneous data and extracting biological relevant information in bioinformatic analyses. %U http://scidok.sulb.uni-saarland.de/volltexte/2017/6930/http://scidok.sulb.uni-saarland.de/doku/lic_ohne_pod.php?la=de
43. Sun P, Guo J, Winnenburg R, Baumbach J: Drug Repurposing by Integrated Literature Mining and Drug–Gene–Disease Triangulation. Drug Discovery Today 2017, 22.
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@article{Sun_Baumbach2017, TITLE = {Drug Repurposing by Integrated Literature Mining and Drug--Gene--Disease Triangulation}, AUTHOR = {Sun, Peng and Guo, Jiong and Winnenburg, Rainer and Baumbach, Jan}, LANGUAGE = {eng}, ISSN = {1359-6446}, DOI = {10.1016/j.drudis.2016.10.008}, PUBLISHER = {Elsevier}, ADDRESS = {Amsterdam}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, JOURNAL = {Drug Discovery Today}, VOLUME = {22}, NUMBER = {4}, PAGES = {615--619}, }
Endnote
%0 Journal Article %A Sun, Peng %A Guo, Jiong %A Winnenburg, Rainer %A Baumbach, Jan %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Drug Repurposing by Integrated Literature Mining and Drug&#8211;Gene&#8211;Disease Triangulation : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-6E09-3 %R 10.1016/j.drudis.2016.10.008 %7 2016-10-22 %D 2017 %J Drug Discovery Today %V 22 %N 4 %& 615 %P 615 - 619 %I Elsevier %C Amsterdam %@ false