Colloquium: Computational Approaches Toward Better Drugs
Abstract: Drug development has been extremely costly and of extremely low success rate. Even after successful development and FDA approval, many marketed drugs do not introduce equal efficacy on different patients. In this talk, we will present how computational approaches can help accelerate drug development and facilitate precision drug selection. In specific, we will discuss a new ranking framework and ranking methods to prioritize drug candidates when multiple criteria are considered (e.g., drug bioactivity and selectivity). We will also discuss a new ranking-based approach to selecting effective cancer drugs for different patients.
Bio: Xia Ning is an Assistant Professor in the Biomedical Informatics Department, The Ohio State University. She received her Ph.D. from University of Minnesota, Twin cities, in 2012. From 2012 to 2014, she worked as a research staff member at NEC Labs, America. From 2014 to 2018, she was an Assistant Professor in the Computer and Information Science Department, Indiana University – Purdue University Indianapolis. She joined OSU in July 2018. Ning’s research is on Data Mining, Machine Learning and Big Data analysis with applications on Chemical Informatics, Bioinformatics, Health Informatics and e-commerce, etc., and has been highly interdisciplinary. In specific, Ning’s research focuses on developing scalable models and computational methods to derive knowledge from heterogeneous Big Data, conduct modeling, ranking, classification and prediction, etc., and ultimately solve critical and high-impact problems. Specific research topics include drug candidate prioritization for drug discovery, cancer drug selection for precision medicine, and information retrieval from electronic medical records.