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Workshop on Computational Biology
Dana Pe'er · Christina Leslie · Barbara Engelhardt · Elham Azizi · Sandhya Prabhakaran · Meghana Kshirsagar · Ambrose Carr

Wed Aug 09 03:30 PM -- 12:30 AM (PDT) @ C4.4
Event URL: https://sites.google.com/view/compbioworkshopicml2017 »

The workshop will showcase recent research in the field of Computational Biology. There has been significant development in genomic sequencing techniques as well as imaging technologies that not only generate huge amounts of data but provide unprecedented levels of resolution, that of a single cell and even subcellular resolution. This availability of high dimensional data, at multiple spatial and temporal resolutions and capturing several perspectives of biological phenomena has made machine learning methods increasingly relevant for computational analysis of the data. Conversely, biological data has also exposed unique challenges and problems that call for the development of new machine learning methods. This workshop aims at bringing in researchers working at the intersection of Machine Learning and Biology to present recent advances and open questions in computational biology to the ICML community.

Wed 3:45 p.m. - 3:50 p.m.
Opening Remarks
Wed 3:50 p.m. - 4:30 p.m.
 link »

Spearman's correlation measures the association between ranked lists. Given a set of ranked lists, we study two tasks: aggregating the set of ranks into one single ranked list, and computing the agreement of the lists as we traverse it. Applications include the analysis of the stability of feature selection and integration of various sources of information. This is illustrated with two examples respectively: We study the stability of identifying variations in GWAS by considering replication studies. In another study, we aggregate genomic distance, 3D associations, and literature information to findpromising disease associated variations. It turns out that these problems can be tackled by considering a multivariate Spearman's correlation.

Wed 4:30 p.m. - 5:00 p.m.
Spotlight Presentations (Spotlight)
Wed 5:00 p.m. - 5:30 p.m.
Coffee Break (Break)
Wed 5:30 p.m. - 6:10 p.m.
Deep learning approaches to impute, integrate and interpret regulatory genomic data (Invited talk)  link »
Wed 6:10 p.m. - 6:30 p.m.
Uncovering the gene usage of human tissue cells with joint factorized embeddings (Contributed Talk)
Wed 6:30 p.m. - 7:00 p.m.
Dilated Convolutions for Modeling Long-Distance Genomic Dependencies (Contributed Talk)
Wed 7:00 p.m. - 9:00 p.m.
Poster Session I (Lunch & Posters)
Wed 9:00 p.m. - 9:40 p.m.
Reasoning from "Messy" Clinical Time Series for Individualizing Care -- Suchi Saria (Invited Talk)  link »
Wed 9:40 p.m. - 10:00 p.m.
Ask the doctor – Improving drug sensitivity predictions through active expert knowledge elicitation (Contributed Talk)
Wed 10:00 p.m. - 11:15 p.m.
Poster Session II (Posters & Break)
Wed 11:15 p.m. - 11:35 p.m.
Contrastive Principal Component Analysis (Contributed Talk)
Wed 11:35 p.m. - 12:00 a.m.
Closing Remarks and Awards (Closing & Awards)

Author Information

Dana Pe'er (Sloan Kettering Institute)
Christina Leslie (Memorial Sloan Kettering Cancer Center)
Barbara Engelhardt (Princeton University)
Elham Azizi (Memorial Sloan Kettering Cancer Center)
Sandhya Prabhakaran (Memorial Sloan Kettering Cancer Centre)
Meghana Kshirsagar (Memorial Sloan Kettering Cancer Center)
Ambrose Carr (Memorial Sloan Kettering Cancer Center)

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