Bayes Regularization and Microbiome Multi-omics: A Two-tailed Narrative

SCHEDULE

Presentation: May 13 2020, 1:00pm – 2:00pm
Where: Zoom Meeting
Presentation Survey: Link Here

ABSTRACT

In this talk, I will present a statistical narrative of two seemingly different but equally exciting topics: Bayes regularization and microbiome multi-omics, drawing upon my PhD and postdoc experience of researching in two vastly different academic settings. In the first part of the talk, I will revisit the first principles of variable selection and discuss regularization from a Bayesian vantage point. Several case studies involving both real and synthetic data will be presented for illustration purposes. In the second part of the talk, I will take a quick detour to discuss the challenges of microbiome multi-omics, which are typically noisy, sparse (zero-inflated), high-dimensional, and extremely non-normal, often arising in the form of count or compositional measurements. I will specifically discuss two recent statistical approaches to data integration in human microbiome studies with an application to the multi-platform genomics data from the recently completed NIH Human Microbiome Project (iHMP). Finally, I will conclude with comments on the promises and implications of scalable Bayes for large-scale multi-omics data integration and inference for translational epidemiology studies and provide some empirical evidence of using multi-omics both as a multi-purpose biomarker and potential therapeutic target in precision medicine.

PRESENTED BY

Himel Mallick

Himel Mallick is a Senior Scientist at Merck Research Laboratories. Prior to joining Merck last year, Himel was a postdoctoral fellow at Harvard and a PhD candidate of Biostatistics at the University of Alabama at Birmingham. Himel has published more than 30 papers in top-tier scientific journals including Nature and Lancet as well as top quantitative journals such as Statistics in Medicine and PLoS Computational Biology. He is a recipient of multiple awards including the ASA Best Student Paper Award (2015), AMS-Simons Grant (2016), and IMS New Researcher Award (2017). Himel’s major research interests include Bayesian analysis, machine learning, statistical genetics and genomics, and computational biology.