报告题目: Integrative Genomics Approach for Precision Medicine in Cancer
报告人: Kun Huang，Indiana University
摘 要: During the past decades, many studies have led to biomarkers for cancer outcome predictions, which assist clinicians on selecting the right treatment strategy. These biomarkers include both histopathological features and various types of omic data. However, there is a lack of a unified means for patient stratification which can effectively integrate the heterogeneous types of molecular and clinical data and improve accuracy on patient outcome prediction. I will present our recent work on integrating multiple types of omic data with histopathological features based on quantitative bioimage analysis using machine learning and visualization approaches.
Dr. Kun Huang received Double Degree in Biological Sciences and Computer Science from Tsinghua University in 1996 and MS degrees in Physiology, Electrical Engineering, and Mathematics all from the University of Illinois at Urbana-Champaign (UIUC). He then received his PhD in Electrical and Computer Engineering from UIUC in 2004 with a focus on computer vision and machine learning. He joined the Department of Biomedical Informatics at The Ohio State University (OSU) where he later served as Director for the Computational Biology and Bioinformatics Division and Associate Dean for Genome Informatics in the College of Medicine. Currently he is Professor in Medicine and Director for Data Science and Informatics of the Precision Health Initiative at Indiana University School of Medicine as well as Assistant Dean for Data Science. His research interests include bioinformatics, computational biology, bioimage informatics, and machine learning. He has co-authored 170 research papers.