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Results for: Publications | Stochastic Modeling Lab

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.

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.

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.

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.

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.

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.

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.

Making the cut: improved ranking and selection for large-scale inference. Henderson NC, Newton MA. Journal of the Royal Statistical Society Series B 78(4):781-804.

Multiset statistics for gene set analysis. Newton MA, Wang Z. Annual Review of Statistics and Its Application 2:95-111, 2015.