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340 Wed, 8/5/2020, 10:00 AM - 2:00 PM Virtual
Novel Methods for Microbiome and Metabolomic Disease — Contributed Papers
Biometrics Section
Chair(s): Joanna Harton, University of Pennsylvania
Powerful and Robust Nonparametric Association Testing for Microbiome Data via a Zero-Inflated Quantile Approach
Wodan Ling, Fred Hutchinson Cancer Research Center; Michael C Wu, Fred Hutchinson Cancer Research Center
Quantifying and Association Testing of Microbiome Alpha Diversity
Shilan Li, NCI; Jianxin Shi, National Cancer Institute
Complex Trait Genetics Through the Lens of Regulatory Networks
Xiang Zhu, Stanford University; Zhana Duren, Stanford University; Wing Hung Wong, Stanford University
WINN: Drift Correction by White Noise Normalization for Metabolomic Studies.
Olga Demler, Harvard Medical School; Franco Giulianini, Harvard University; Samia Mora, Harvard University
Flexible Dynamic Models for Longitudinal Microbiome Data
Soo-Young Kim, Fred Hutchinson Cancer Research Center; Ollivier Hyrien, Fred Hutchinson Cancer Research Center; David Fredricks, Fred Hutchinson Cancer Research Center; Sujatha Srinivasan, Fred Hutchinson Cancer Research Center
Differential Abundance Analysis with Bayes Shrinkage Estimation of Variance (DASEV) for Zero-Inflated Proteomic and Metabolomic Data
Chi Wang, University of Kentucky; Zhengyan Huang, University of Kentucky; Andrew Lane, University of Kentucky; Teresa Fan, University of Kentucky; Rick Higashi, University of Kentucky; Heidi Weiss, University of Kentucky; Xiangrong Yin, University of Kentucky
Using an Estimated Sparse Two-Level Gene-Pathway Network for Quadratic Discriminant Analysis to Predict Breast Cancer Patients’ Treatment Responses
Liang Shan, UAB; Dongquan Chen, UAB; Inyoung Kim, Virginia Tech; Sejong Bae, UAB