Stochastic Modeling Lab

This lab is focused on designing, refining and inferring model structures in tasks with large, heterogeneous data sources. The lab is also developing methodologies for error-rate control and prioritization in ranking, selection and data integration tasks.

Related CPCP Publications

Machine learning consensus scoring improves performance across targets in structure-based virtual screening. Ericksen S, Wu H, Zhang H, Michael L, Newton M, Hoffmann FM, Wildman S. Journal of Chemical Information and Modeling 57(7):1579–1590, 2017

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SCnorm: robust normalization of single-cell RNA-seq data. Bacher R, Chu LF, Leng N, Gasch A, Thomson J, Stewart R, Newton M, Kendziorski C. Nature Methods, 2017

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A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Korthauer K, Chu LF, Newton M, Li Y, Thomson J, Stewart R, Kendziorski K. Genome Biology 17:222, 2016

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Subclonal diversity arises early even in small colorectal tumours and contributes to differential growth fates. Sievers C, Zou L, Pickhardt P, Matkowskyj K, Albrecht D, Clipson L, Bacher J, Pooler BD, Moawad F, Cash B, Reichelderfer M, Vo T, Newton M, Larget B, Halberg R. Gut, 2016

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Further support for aneuploidy tolerance in wild yeast and effects of dosage compensation on gene copy-number evolution. Gasch A, Hose J, Newton M, Sardi M, Yong M, Wang Z. eLife 5:e14409, 2016

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Lead

Michael Newton

Investigators

Xiuyu Ma

Tien Vo

Huikun Zhang

Resources

CPCP 2017 Retreat: Improving Target-Ligand Activity Predictions by Combining Multiple Docking Scores Symposium Video

Rolemodel software

rvalues software