Statistics and Machine Learning
Dr. Sach Mukherjee
Group Leader
Venusberg-Campus 1, Gebäude 99
(ehemals Sigmund-Freud-Str. 27)
53127 Bonn

sach.mukherjee@dzne.de
 +49 228 43302-853

Areas of investigation/research focus

We work at the interface between the hard data sciences (computational statistics and machine learning) and biomedicine. Our long-term goal is to deeply integrate the hard data sciences with systems approaches to disease biology and medicine. To this end we have over several years studied key statistical issues that arise in complex, high-dimensional biomedical data. At the DZNE our focus is on prediction, stratification and systems analyses for neurodegenerative diseases. Working with colleagues across the DZNE (in fundamental, population and clinical research) and internationally, we are developing and applying approaches at the statistical frontier to help realize the promise of next-generation biomedicine.

Advances in high-throughput molecular assays and deep phenotyping coupled with systems-level analyses have the potential to transform biomedical research. Such approaches can inform stratification into disease subtypes, allow prediction of disease state and help elucidate relevant biology at a systems level. Our efforts are directed towards developing high-dimensional statistical andmachine learning methods to realize this potential. This involves working on novel methods motivated by, and applied to, specific applications but also working with colleagues across research areas to clarify conceptual issues that arise in moving towards truly scalable and data-intensive approaches. In the area of systems biology, we are currently working on principled yet highly scalable approaches by which to build and test global molecular networks that are specific to biological or disease context. Furthermore, we are working on methods for prediction and stratification of neurodegenerative diseases, with an emphasis on integrative analyses using diverse high-dimensional data types.

Key Publications

Steven M. Hill, Nicole K. Nesser, Katie Johnson-Camacho, Mara Jeffress, Aimee Johnson, Chris Boniface, Simon E.F. Spencer, Yiling Lu, Laura M. Heiser, Yancey Lawrence, Nupur T. Pande, James E. Korkola, Joe W. Gray, Gordon B. Mills, Sach Mukherjee, Paul T. Spellman. Context Specificity in Causal Signaling Networks Revealed by Phosphoprotein Profiling. Cell Systems. 2017 Jan 24; 4:73-83.e10. doi: 10.1016/j.cels.2016.11.013
Städler N and Mukherjee S. Two-sample testing in high dimensions. J. R. Stat. Soc. Series B. 2017 Jan 01; 79 doi: 10.1111/rssb.12173
Steven M. Hill, Laura M. Heiser, Thomas Cokelaer, Michael Linger, Nicole K. Nesser, Daniel E. Carlin, Yang Zhang, Artem Sokolov, Evan O. Paull, Chris K. Wong, Kiley Graim, Adrian Bivol, Haizhou Wang, Fan Zhu, Bahman Afsari, Ludmila V. Danilova, Alexander V. Favorov, Wai Shing Lee, Dane Taylor, Chenyue W. Hu, Byron L. Long, David P. Noren, Alexander J. Bisberg, Gordon B. Mills, Joe W. Gray, Michael Kellen, Thea Norman, Stephen Friend, Amina A. Qutub, Elana J. Fertig, Yuanfang Guan, Mingzhou Song, Joshua M. Stuart, Paul T. Spellman, Heinz Koeppl, Gustavo Stolovitzky, Julio Saez-Rodriguez, Sach Mukherjee, Rami Al-Ouran, Bernat Anton, Tomasz Arodz, Omid Askari Sichani, Neda Bagheri, Noah Berlow, Anwesha Bohler, Jaume Bonet, Richard Bonneau, Gungor Budak, Razvan Bunescu, Mehmet Caglar, Binghuang Cai, Chunhui Cai, Azzurra Carlon, Lujia Chen, Mark F. Ciaccio, Gregory Cooper, Susan Coort, Chad J. Creighton, Seyed-Mohammad-Hadi Daneshmand, Alberto De La Fuente, Barbara Di Camillo, Joyeeta Dutta-Moscato, Kevin Emmett, Chris Evelo, Mohammad-Kasim H. Fassia, Francesca Finotello, Justin D. Finkle, Xi Gao, Jean Gao, Samik Ghosh, Alberto Giaretta, Ruth Großeholz, Justin Guinney, Christoph Hafemeister, Oliver Hahn, Saad Haider, Takeshi Hase, Jay Hodgson, Bruce Hoff, Chih Hao Hsu, Ying Hu, Xun Huang, Mahdi Jalili, Xia Jiang, Tim Kacprowski, Lars Kaderali, Mingon Kang, Venkateshan Kannan, Kaito Kikuchi, Dong-Chul Kim, Hiroaki Kitano, Bettina Knapp, George Komatsoulis, Andreas Krämer, Miron Bartosz Kursa, Martina Kutmon, Yichao Li, Xiaoyu Liang, Zhaoqi Liu, Yu Liu, Songjian Lu, Xinghua Lu, Marco Manfrini, Marta R. A. Matos, Daoud Meerzaman, Wenwen Min, Christian Lorenz Müller, Richard E. Neapolitan, Baldo Oliva, Stephen Obol Opiyo, Ranadip Pal, Aljoscha Palinkas, Joan Planas-Iglesias, Daniel Poglayen, Francesco Sambo, Tiziana Sanavia, Ali Sharifi-Zarchi, Janusz Slawek, Adam Streck, Sonja Strunz, Jesper Tegnér, Kirste Thobe, Gianna Maria Toffolo, Emanuele Trifoglio, Michael Unger, Qian Wan, Lonnie Welch, Jia J. Wu, Albert Y. Xue, Ryota Yamanaka, Chunhua Yan, Sakellarios Zairis, Michael Zengerling, Hector Zenil, Zhike Zi. Inferring causal molecular networks: Empirical assessment through a community-based effort. Nature Methods. 2016 Mar 29; 13:310-322. doi: 10.1038/nmeth.3773
Robert J. B. Goudie, Sach Mukherjee. A Gibbs sampler for learning DAGs. Journal of Machine Learning Research. 2016 Mar 31; 17
Oates CJ, Dondelinger F, Bayani N, Korkola J, Gray JW and Mukherjee S. Causal network inference using biochemical kinetics. Bioinformatics. 2014 Sep 01; 30:i468-74. doi: 10.1093/bioinformatics/btu452

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