Integrative Discriminant Analysis Methods for Multi-view Data
Many diseases are complex heterogeneous conditions that affect multiple organs in the body and depend on the interplay between several factors that include molecular and environmental factors, thus requiring a holistic approach in understanding the complexity and heterogeneity. In this talk, I will present some of our current statistical and machine learning methods for integrating data from multiple sources while simultaneously classifying units or individuals into one of multiple classes or disease groups. The proposed methods are tested using both simulated data and real-world datasets, including RNA sequencing, metabolomics, and proteomics data pertaining to COVID-19 severity. We identified signatures that better discriminated COVID-19 patient groups, and related to neurological conditions, cancer, and metabolic diseases, corroborating current research findings and heightening the need to study the post sequelae effects of COVID-19 to devise effective treatments and to improve patient care.
Sandra Safo is an Assistant Professor of Biostatistics at the University of Minnesota. She is interested in developing statistical learning, data integration, and feature selection methods for high-dimensional data. Currently, she develops methods for integrative analysis of “omics” (including genomics, transcriptomics, and metabolomics) and clinical data to help elucidate the complex interactions of these multifaceted data types.