MASTER OF SCIENCE PROJECT DEFENSE BY: Xiaochun Chen
When: Tuesday,
May 16, 2017
11:00 AM
-
1:00 PM
Where: Science & Engineering Building, Lester W. Cory Conference Room: Room 213A
Cost: Free
Description: TOPIC: CLUSTERING CANCER GENOMIC DATA
LOCATION: Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A
ABSTRACT:
Clustering is an unsupervised learning. Clustering aims to group data with similar pattern into the same cluster. Cancer is a large group of diseases and a leading cause of death worldwide. In cancer research and clinics, clustering has been widely used to identify cancer heterogeneity and subgroups. There are different kinds of clustering methods in the literature. Each of them has its advantages and disadvantages in terms of a specific application. In this project, I investigated some of existing clustering methods, such as K-means, hierarchical, spectral, and consensus clustering, and then chose appropriate methods for the cancer applications. I applied these methods for clustering cancer genomic datasets and subgroup identification and showed promising results.
NOTE: All ECE Graduate Students are ENCOURAGED to attend.
All interested parties are invited to attend. Open to the public.
Advisor: Dr. Honggang Wang
Committee Members:
Dr. Liudong Xing, Department of Electrical & Computer Engineering and Dr. Donghui Yan, Department of Mathematics
*For further information, please contact Dr. Honggang Wang at 508.999.8469, or via email at hwang1@umassd.edu.
LOCATION: Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A
ABSTRACT:
Clustering is an unsupervised learning. Clustering aims to group data with similar pattern into the same cluster. Cancer is a large group of diseases and a leading cause of death worldwide. In cancer research and clinics, clustering has been widely used to identify cancer heterogeneity and subgroups. There are different kinds of clustering methods in the literature. Each of them has its advantages and disadvantages in terms of a specific application. In this project, I investigated some of existing clustering methods, such as K-means, hierarchical, spectral, and consensus clustering, and then chose appropriate methods for the cancer applications. I applied these methods for clustering cancer genomic datasets and subgroup identification and showed promising results.
NOTE: All ECE Graduate Students are ENCOURAGED to attend.
All interested parties are invited to attend. Open to the public.
Advisor: Dr. Honggang Wang
Committee Members:
Dr. Liudong Xing, Department of Electrical & Computer Engineering and Dr. Donghui Yan, Department of Mathematics
*For further information, please contact Dr. Honggang Wang at 508.999.8469, or via email at hwang1@umassd.edu.
Contact:
ECE: Electrical & Computer Engineering Department 508.999.9164 http://www.umassd.edu/engineering/ece/
Topical Areas: General Public, University Community, College of Engineering, Electrical and Computer Engineering