报告题目：Integration of Imaging and Genomics Data for Precision Medicine (面向精准医疗的图像和基因数据融合)
报告摘要: During the past decades, many studies have led to biomarkers for cancer outcome predictions, which assist clinicians on selecting the effective treatment strategy. These biomarkers include both histopathological attributes and various types of omic data. However, there is a lack of a unified means for patient stratification that can effectively integrate the heterogeneous types of molecular and clinical data and improve accuracy on patient outcome prediction. In this presentation, we will discuss the progress and challenges on integrating multiple types of omic data with imaging, especially histophathology images for cancer patient stratification. We identify three types of integration strategies including correlative analysis, sparse modeling methods, and consensus learning. The applications of these approaches in cancer studies lead to new hypothesis on gene functions and molecular basis of cell and tissue morphology as well as potential new markers for cancer patient precision medicine.
报告人简介: Dr. Kun Huang received his BS and BE degrees in Biological Sciences and Computer Science from Tsinghua University in 1996 and his 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. Currently he is a Professor and Division Director for Computational Biology and Bioinformatics in the Department of Biomedical Informatics at The Ohio State University (OSU). He is also the Associate Dean for Genome Informatics in the College of Medicine leading the development of precision medicine programs. His research interests include bioinformatics, computational biology, bioimage informatics, and machine learning. He has published more than 160 papers.