Big Data Analytics and a Path to Enhancing Our Understanding of Human Disease

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Dr. Schadt provides an overview of how his team organizes very large scale data across many different types, and then integrates these data using sophisticated mathematical algorithms to construct predictive network models of disease. This approach well complements the type of natural artificial intelligence/machine learning approaches that have become nearly standard in the life and biomedical sciences for building classifiers for a whole range of problems (disease classification, subtype classification, drug response classification, and so on). By building a causal network model that spans multiple scales (from the molecular to the cellular, to the tissue/organ, to the organism and community) we can understand the flow of information and how best to modulate that flow to improve human wellbeing, whether better diagnosing and treating disease or improving overall health. Examples of building these predictive network models across a range of diseases are given, covering areas such as Alzheimer’s disease, inflammatory bowel disease, and diabetes.

Dr. Schadt also discusses the application of this type of modeling in the cancer arena where interpreting any given cancer case in the context of the digital universe of information relating to the cancer of interest is carried out using predictive network models to inform on personalized treatment options for a given patient, including personalized vaccines or novel drug combinations.

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About the Speaker

Dr. Schadt is Chair of the Department of Genetics and Genomic Sciences, Director of the Icahn Institute for Genomics and Multiscale Biology, and Jean C. & James W. Crystal Professor of Genomics at the Icahn School of Medicine at Mount Sinai. Dr. Schadt’s lab and broader efforts at the Icahn Institute focus on the integration of diverse, very large scale data to build predictive models of disease that can aid in providing more accurate diagnoses of disease and how best to treat it.

Dr. Schadt is an expert on the generation and integration of very large-scale sequence variation, molecular profiling and clinical data in disease populations for constructing molecular networks that define disease states and link molecular biology to physiology. He is known for calling for a shift in molecular biology toward a network-oriented view of living systems to complement the reductionist, single-gene approaches that currently dominate biology in order to more accurately model the complexity of biological systems. Dr. Schadt’s research has provided novel insights into what is needed to master diverse, large-scale data collected on normal and disease populations in order to elucidate the complexity of disease and make more informed decisions in the drug discovery arena. He has published more than 200 peer-reviewed papers in leading scientific journals, and contributed to a number of discoveries relating to the genetic basis of common human diseases such as diabetes, obesity, and Alzheimer’s disease.

Dr. Schadt is also a founding member of Sage Bionetworks, an open-access genomics initiative designed to build and support databases and an accessible platform for creating innovative dynamic disease models. Prior to joining Mount Sinai in 2011, he was Chief Scientific Officer at Pacific Biosciences, the next-generation sequencing technology provider. Previously, Dr. Schadt was Executive Scientific Director of Genetics at Rosetta Inpharmatics, a subsidiary of Merck & Co., Inc. in Seattle, and before Rosetta, Dr. Schadt was a Senior Research Scientist at Roche Bioscience. He received his B.A. in applied mathematics and computer science from California Polytechnic State University, his M.A. in pure mathematics from University of California, Davis, and his Ph.D. in bio-mathematics from University of California, Los Angeles (requiring Ph.D. candidacy in molecular biology and mathematics).

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