Announcements

MATHEMATICS SEMINAR by Öznur Taştan

Author: COLLEGE OF SCIENCES
Time: 14:30
Location: ENG B29

KOÇ UNIVERSITY MBGE SEMINAR
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Speaker          :
Öznur Taştan, Sabancı Üniversitesi

Title                : DeepKinZero: Zero-Shot Learning for Predicting Kinase-Phosphosite Associations Involving Understudied Kinases

Date                : November 13, 2019, Wednesday
Time               : 14:30  
Cookie & Tea: 14:15 ENG B29
Place               : ENG B29


Abstract         : Protein phosphorylation is a key regulator of protein function in signal transduction pathways. Kinases are the enzymes that catalyze the phosphorylation of other proteins in a target specific manner. The dysregulation of phosphorylation is associated with many diseases including cancer. Although the advances in phosphoproteomics enable the identification of phosphosites at the proteome level, most of the phosphoproteome is still in the dark:  more than 95 % of the reported human phosphosites have no known kinases. Determining which kinase is responsible for phosphorylating a site remains an experimental challenge. Existing computational methods require several examples of known targets of a kinase to make accurate kinase specific predictions, yet for a large body of kinases, only a few or no target sites are reported. In this talk I will present DeepKinZero, the first zero-shot learning approach to predict the kinase acting on a phosphosite for kinases with no known phosphosite information. DeepKinZero transfers knowledge from kinases with many known target phosphosites to those kinases with no known sites through a zero-shot learning model. The kinase specific positional amino acid preferences are learned using a bidirectional recurrent neural network. We show that DeepKinZero achieves significant improvement in accuracy for kinases with no known phosphosites in comparison to other methods available. By expanding our knowledge on understudied kinases, DeepKinZero can help to chart the phosphoproteome atlas.


Short Bio       : Dr. Tastan holds a BSc in Biological Sciences and Bioengineering from Sabanci University and received her PhD in 2011, from Carnegie Mellon University, School of Computer Science. Since 2018, she has been with the Sabanci University Computer Science and Engineering and Molecular Biology, Genetics and Bioengineering departments. Before joining to Sabanci, she worked as faculty member at Bilkent University, Department of Computer Engineering, a post-doctoral researcher at Microsoft Research New England Lab (Cambridge, MA, USA). She is a recipient of the Young Scientist (BAGEP) Research Award of the Science Academy, Turkey and UNESCO-L’OREAL National Fellowship for Young Women in Life Sciences. She has worked on diverse problems in computational biology; her present efforts center on building machine learning models to advance the current understanding of complex diseases.