Knowledge-based Biomedical Data Science

Abstract

Knowledge-based biomedical Data Science involves the design and implementation of computer systems that act as if they knew about biomedicine. There are many ways in which a computational approach might act as if it knew something: for example, it might be able to answer a natural language question about a biomedical topic, or pass an exam; it might be able to use existing biomedical knowledge to rank or evaluate hypotheses; it might explain or interpret data in light of prior knowledge, either in a Bayesian or other sort of framework. These are all examples of automated reasoning that act on computational representations of knowledge. After a brief survey of existing approaches to knowledge- based data science, I will describe some recent results from my laboratory involving comparison of alternative approaches to knowledge graph construction, vector space embeddings derived from knowledge graphs, and the use of knowledge graphs to elucidate molecular features implicit in electronic health records.

Bio:

Dr. Lawrence Hunter is the Director of the University of Colorado's Computational Bioscience Program and a Professor of Pharmacology (School of Medicine) and Computer Science (Boulder). He received a Ph.D. in computer science from Yale University in 1989, and then joined the National Institutes of Health as a staff scientist, first at the National Library of Medicine and then at the National Cancer Institute, before coming to Colorado in 2000. Dr. Hunter is widely recognized as one of the founders of bioinformatics; he served as the first President of the International Society for Computational Biology (ISCB), and created several of the most important conferences in the field, including ISMB, PSB and VizBi. Dr. Hunter's research interests span a wide range of areas, from cognitive science to rational drug design. He has published more than 100 scientific papers, holds two patents and has been elected a fellow of both the ISCB and the American College of Medical Informatics. His primary focus recently has been the integration of natural language processing, knowledge representation, machine learning and advanced visualization techniques to address challenges in interpreting data generated by high throughput molecular biology.

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