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Imaging Genomics Approach for Cancer Patient Stratification
Date: 2014/10/31             Browse: 567

Speaker: Kun Huang

Time: Oct. 31, 10:15am-11:30am

Location: Room 220, Building 8

Abstract:

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 attributes 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 as well as a newly developed consensus clustering algorithm.

Bio:

Dr. Kun Huang received his BS degree in Biological Sciences 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 an Associate Professor in the Department of Biomedical Informatics at The Ohio State University (OSU). His research interests include bioinformatics, computational biology, bioimage informatics, and machine learning. He has co-authored more than 130 papers.

                                                                                                                              SIST-Seminar 14033