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

Riemannian variance filtering: An independent filtering scheme for statistical tests on manifold-valued data. Zheng L, Kim HJ, Adluru N, Newton MA, Singh V. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017

Publication details

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

Publication details

Identification of tissue‐specific transcriptional markers of caloric restriction in the mouse and their use to evaluate caloric restriction mimetics. Barger JL, Vann JM, Cray N, Pugh TD, Mastaloudis A, Hester SN, Wood SN, Newton MA, Weindruch R, Prolla TA. Aging Cell 16:750-760, 2017

Publication details

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 14:584–586, 2017

Publication details

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

Publication details

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