Shubham Tripathi, PhD
Yale-Boehringer Ingelheim Biomedical Data Science Fellow '22
Topic: Mechanism-based identification of biomarkers and intervention targets from multi-omics datasets
Project Summary: Recent technological advances allowing for the global characterization of genomic variants, transcription profiles, epigenomic profiles, and protein markers, often down to the single-cell level, have provided unprecedented insights into the homeostatic and perturbed states of biological systems. Analyzing these vast multi-omics datasets to obtain clinically actionable biomarkers and promising intervention targets remains a formidable challenge. Prediction of the individual immune response quality and quantity in health and disease is one quintessential case. Our proposed research will combine statistical analyses with causal inference and multi-scale mathematical modeling to develop a multi-omics data analysis pipeline that (a) provides mechanistic insights into the underlying biological process, (b) captures the diversity seen across individuals, and (c) identifies complex features and rules that are predictive of the response to perturbations. We will apply our approach to datasets characterizing the vaccination response to identify predictive biomarkers and intervention targets to improve vaccine efficacy.