Biomedical Data Science for Precision Medicine Vanathi Gopalakrishnan, PhD Associate Professor of Biomedical Informatics, Intelligent Systems and Computational & Systems Biology Director, PRoBE Laboratory for Pattern Recognition from Biomedical Evidence Department of Biomedical Informatics School of Medicine University of Pittsburgh

PRoBE Lab: Research Interests Overall Goal: To Accelerate Biomedical Knowledge Discovery PRIOR KNOWLEDGE DATA RULE LEARNING METHODOLOGIES Example: Transfer Rule Learning project funded by NIGMS R01GM100387 to VG PREDICTIVE MODELS FOR BIOMEDICINE Enable predictions about for example: mechanism of action, target selection, useful biomarkers, toxicity, and clinical trial outcomes.

DATA WE WORK WITH From biomarker* discovery studies for early detection of: Amyotrophic Lateral Sclerosis Alzheimer’s Lung Cancer Breast Cancer Esophageal Cancer Inflammatory Bowel Diseases Ulcerative Colitis, Crohn’s Coronary Artery Disease Pediatric Cardiomyopathy Proteomic Mass Spectral (large/wide/deep) Immunoassay Genomic GWAS/SNPs(very large) DNA Methylation Gene Expression microRNA Images Cardiac MRI Brain fMRI *The term “biomarker” or “biological marker”, refers to “a broad subcategory of medical signs – that is, objective indications of medical state observed from outside the patient – which can be measured accurately and reproducibly. Medical signs stand in contrast to medical symptoms, which are limited to those indications of health or illness perceived by patients themselves.” - Kyle Strimbu, Jorge A. Tavel. What are Biomarkers? Curr Opin HIV AIDS. 2010 Nov 5(6): 463–466.

Example: Lung Cancer Early Detection RISK PREDICTION MODEL Clinical History ? Accuracy of early detection # Unnecessary biopsies CT screen Results Blood Test Multiplexed serologic Quantitative immunoassays Luminex xMAP® technology UPCI Lung Cancer SPORE Project funded by the NCI using P50 mechanism: VG co-directs the Biostatistics and Bioinformatics Core