{"id":331649,"date":"2011-01-11T05:42:26","date_gmt":"2011-01-11T13:42:26","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-group&p=331649"},"modified":"2020-04-15T15:07:57","modified_gmt":"2020-04-15T22:07:57","slug":"computational-biology-at-msr-new-england","status":"publish","type":"msr-group","link":"https:\/\/www.microsoft.com\/en-us\/research\/group\/computational-biology-at-msr-new-england\/","title":{"rendered":"Computational Biology at MSR New England"},"content":{"rendered":"

We encompass several\u00a0approaches to computational biology: we try to frame the biological question under consideration in terms of more standard problems in computer science, like clustering, Steiner trees, flow problems, etc., and then use approximation algorithms motivated by statistical physics to solve these problems.\u00a0One of our most successful approaches in this realm\u00a0involves variants of belief- and survey propagation algorithms, but in the course of adapting our problem to this setting, we often need to derive alternative representations of the original computer science problem which might be useful when applying other algorithms as well.<\/p>\n

We also approach many problems from the perspective of applied statistics and machine learning, making use of latent variable models and efficient operations on them to perform inference and learning. In this vein, we have tackled problems in CRISPR gene editing (opens in new tab)<\/span><\/a>; \u00a0problems in statistical genetics such as effective and efficient handling of unknown confounding factors in eQTL association studies, genome-wide association studies, and analysis of methylation data; immunoinformatics such as HLA imputation and refinement, epitope prediction; problems in proteomics such as\u00a0alignment of vector time series resulting from liquid-chromatography-mass-spectrometry systems.<\/p>\n

We are also contributing our machine learning expertise to collaborations with leading biologists and clinicians focusing on better understanding and harnessing the power of the human immune system.\u00a0 We are partnering with immuno-oncologists on several research projects funded by Stand Up to Cancer (opens in new tab)<\/span><\/a>, and with Adaptive Biotechnologies to decode the logic of the immune system (opens in new tab)<\/span><\/a>.<\/p>\n

For more information, please follow the links to our individual web pages.<\/p>\n","protected":false},"excerpt":{"rendered":"

We encompass several\u00a0approaches to computational biology: we try to frame the biological question under consideration in terms of more standard problems in computer science, like clustering, Steiner trees, flow problems, etc., and then use approximation algorithms motivated by statistical physics to solve these problems.\u00a0<\/p>\n","protected":false},"featured_media":267135,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr_group_start":"","footnotes":""},"research-area":[13561,13556,13546,13553],"msr-group-type":[243694],"msr-locale":[268875],"msr-impact-theme":[],"class_list":["post-331649","msr-group","type-msr-group","status-publish","has-post-thumbnail","hentry","msr-research-area-algorithms","msr-research-area-artificial-intelligence","msr-research-area-computational-sciences-mathematics","msr-research-area-medical-health-genomics","msr-group-type-group","msr-locale-en_us"],"msr_group_start":"","msr_detailed_description":"","msr_further_details":"","msr_hero_images":[],"msr_research_lab":[199563],"related-researchers":[{"type":"user_nicename","display_name":"Miro Dud\u00edk","user_id":32867,"people_section":"Group 1","alias":"mdudik"},{"type":"user_nicename","display_name":"Nicolo Fusi","user_id":31829,"people_section":"Group 1","alias":"fusi"},{"type":"user_nicename","display_name":"Sharon Gillett","user_id":33599,"people_section":"Group 1","alias":"sharong"},{"type":"user_nicename","display_name":"Lester Mackey","user_id":36161,"people_section":"Group 1","alias":"lmackey"},{"type":"user_nicename","display_name":"Philip Rosenfield","user_id":37562,"people_section":"Group 1","alias":"phrosenf"},{"type":"guest","display_name":"Luca Saglietti","user_id":453387,"people_section":"Group 1","alias":""}],"related-publications":[],"related-downloads":[],"related-videos":[],"related-projects":[502523,292757],"related-events":[],"related-opportunities":[],"related-posts":[],"tab-content":[{"id":0,"name":"Selected Publications","content":"

\r\n\r\nOptimized sgRNA design to maximize activity and minimize off-target effects for genetic screens with CRISPR-Cas9<\/a>\r\n<\/b>JG Doench*, N Fusi*, M Sullender*, M Hegde*, EW Vaimberg*, KF Donovan, I Smith, Z Tothova, C Wilen , R Orchard , HW Virgin, J Listgarten*, DE Root, Nature Biotechnology<\/i> (2016)\r\n\r\nWarped linear mixed models for the genetic analysis of transformed phenotypes<\/a>\r\n<\/b>Fusi F., Lippert C., Lawrence N., Stegle O, Nature Communications <\/i>(2014)\r\n\r\nEpigenome-wide association studies without the need for cell-type composition<\/b><\/a> <\/b>\r\nZou J, Lippert C, Heckerman D, Aryee, M, Listgarten J Nature Methods<\/i>, 309\u2013311 (2014)\r\n\r\nFaST-LMM-Select for addressing confounding from spatial structure and rare variants<\/b><\/a>\r\nListgarten* J, Lippert* C, Heckerman* D (*equal contributions) Nature Genetics,<\/i> 45, 470-471 (2013)\r\n\r\nImproved linear mixed models for genome-wide association studies<\/b><\/a> <\/b>\r\nListgarten J*, Lippert* C, Kadie C, Davidson B, Eskin E, Heckerman* D *(equal contributions)\r\nNature Methods, 2012<\/i>\r\n\r\nFaST Linear Mixed Models for Genome-Wide Association Studies<\/b><\/a> <\/b>\r\nLippert* C, Listgarten* J., Liu Y, Kadie C, Davidson R, Heckerman* D. (*equal contributions) Nature Methods,<\/i> Aug. 2011\r\n\r\nCorrection for Hidden Confounders in the Genetic Analysis of Gene Expression<\/b> <\/b>\r\n<\/a>Listgarten J, Kadie C, Schadt E, Heckerman D\r\nProceedings of the National Academy of Sciences,<\/i> September 1, 2010\r\n\r\nStatistical resolution of ambiguous HLA typing data<\/b><\/a> <\/b>\r\nListgarten J, Brumme Z, Kadie C, Xiaojiang G, Walker B, Carrington M, Goulder P, Heckerman D, PLoS Computational Biology (<\/i>2008)\r\n\r\nStatistical and computational methods for comparative proteomic profiling using liquid chromatography-tandem mass spectrometry<\/b><\/a> <\/b>Listgarten J and Emili A, Molecular and Cellular Proteomics <\/em>(2005)\r\n\r\nSimultaneous reconstruction of multiple signaling pathways via the prize-collecting Steiner forest problem<\/b><\/a> (N. Tuncbag, A. Braunstein, A. Pagnani, S.S. Huang, J. Chayes, C. Borgs, R. Zecchina, and E. Fraenkel) Journal of Computational Biology <\/em>20 (2013) 124 \u2013 136.\r\n\r\nFinding undetected protein associations in cell signaling by belief prop<\/b>agation<\/b><\/a> (with M. Bailly-Bechet, C. Borgs, A. Braunstein, J. Chayes, A. Dagkessamanskaia, J. Francois, and R. Zecchina). Proceedings of the National Academy of Sciences <\/i>(PNAS) <\/i>108 (2011) 882 \u2013 887.\r\n\r\nStatistical mechanics of Steiner trees<\/b><\/a> <\/b>(M. Bayati, C. Borgs, A. Braunstein, A. Ramezanpour, and R. Zecchina, Physical Review Letters<\/i> 101, 037208 (2008), reprinted in Virtual Journal of Biological Physics Research <\/i>16, August 1 (2008)\r\n\r\n<\/div>"},{"id":1,"name":"Collaborators","content":"
\r\n\r\n\"\"\r\nErnest Fraenkel, MIT<\/b>\r\nErnest Fraenkel studied Chemistry and Physics as an undergraduate at Harvard College and obtained his Ph.D. in Structural Biology at MIT in the department of Biology. After doing post-doctoral work in the same field at Harvard, he turned his attention to the emerging field of Systems Biology. His research now focuses on using high-throughput techniques and computational methods to uncover the molecular pathways that are altered in disease and to identify new therapeutic strategies. Read more...<\/a>\r\n\r\n\"\"\r\nRiccardo Zecchina, Politecnico di Torino, Italy<\/b>\r\nRiccardo is Professor of Theoretical Physics at the Politecnico di Torino in Italy. His interests are in topics at the interface between Statistical Physics and Computer Science. His current research activity is focused on combinatorial and stochastic optimization, probabilistic and message-passing algorithms and interdisciplinary applications of statistical physics (in computational biology, graphical games and statistical inference). Read more...<\/a>\r\n\r\n<\/div>"}],"msr_impact_theme":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-group\/331649","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-group"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-group"}],"version-history":[{"count":8,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-group\/331649\/revisions"}],"predecessor-version":[{"id":342323,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-group\/331649\/revisions\/342323"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/267135"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=331649"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=331649"},{"taxonomy":"msr-group-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-group-type?post=331649"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=331649"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=331649"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}