GSPS: Gene Set-based Patient Subtyping
Tarih ve Saat
6 Mayıs 2019 - 16:00
Identifying cancer subtypes is important for providing personalized treatment effectively, developing new drugs, characterizing risk factors, and understanding the underlying mechanisms of diseases. We present a clustering algorithm that uses multiple kernels defined on pathways/gene sets for identifying cancer subtypes. Our algorithm employs an efficient decomposition algorithm for solving large scale optimization problems within the localized multiple kernel k-means clustering and provides a standalone framework for obtaining patient subtypes on cancer cohorts.
Biography: Oğuz Can Binatlı is a Ph.D. candidate in Industrial Engineering and Operations Management at Koç University, advised by Asst. Prof. Mehmet Gönen. After graduating from İzmir High School of Science, he received his B.Sc. degree in Industrial Engineering from Koç University. He was also a student at Rutgers University and Lehigh University. His research interests lie primarily in the area of data science and machine learning.