About this Class

Presenter: Himel Mallick, PhD Senior Scientist, Biostatistics Merck Research Laboratories Abstract Identifying clinically actionable features that display differential abundance and expression patterns across experimental conditions is an important first step toward characterizing the multi-omics landscape of complex human diseases. The field of multi-omics, however, has not yet reached the maturity attained in other established molecular epidemiology fields such as cancer biomarker discovery and genome-wide association studies with best practices and centralized resources remaining scarce. This is challenging because many standard single-omics analysis methods cannot be directly applied to multi-omics data without falling prey to false positive or false negative results and realistically complex yet flexible modeling techniques must be developed to adequately reflect the biology. In this talk, I will focus on statistical modeling for multi-omics classification and regression including methods for differential analysis, batch effect correction, and machine learning models to enable better disease outcome prediction and patient stratification. I will discuss recently developed statistical methods ranging from Bayesian ensemble methods to sparse graphical models as well as self-adaptive models that adapt to the underlying technological variability in multi-omics data while improving upon state-of-the-art single-omics analysis methods. Lastly, 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. All these approaches will be illustrated on data arising through various multi-omics and single-omics public datasets including the integrative Human Microbiome Project. Join Zoom Meeting Phone one-tap: US: +16692545252,,1601415473#,,,,*357830# or +16468287666,,1601415473#,,,,*357830# Meeting URL: https://nih.zoomgov.com/j/1601415473?pwd=ZHpLOHE3UXo4Y2N0enllK0ZTRTNPQT09 Meeting ID: 160 141 5473 Passcode: 357830