Bilkent University
Department of Computer Engineering
S E M I N A R
Knowledge Discovery on Omics Data Using Biochemical Networks
Dr. Ercüment Çiçek
School of Computer Science
Carnegie Mellon University, USA
Data is the bridge between computer science and biology. The rise of high-throughput omics technologies, which detect and identify biological molecules, came with the need for algorithms and systems to analyze the output. However, knowledge discovery on a high-dimensional space, with relatively small sample sizes and sparse data is a very challenging task. Biochemical networks come in handy to relate biological variables and integrate different data types. Network based analyses benefit from pruned search space, a heuristic map to guide the search and power of incorporating multiple data sources at the same time. In this talk, I am going to introduce several important research questions in computational biology. I am going to describe algorithms like ADEMA, MIRA and DAWN which use biochemical networks to discover biomarkers that are predictive of a diseases such as cystic fibrosis, autism and cancer. I am also going to introduce online systems within the PathCase project, which are used worldwide by life scientists to access biochemical networks and analyze their data.
Bio:
Ercument Cicek has earned his BS (2007) and MS (2009) degrees in Computer Science and Engineering from Sabanci University. He received his Ph.D. degree in Computer Science from Case Western Reserve University, in 2013. During his Ph.D., he has visited Cold Spring Harbor Laboratory to work on analyzing copy number variations in Autism Spectrum Disorder in 2012. After graduation, he has been awarded the Lane Fellowship in Computational Biology by the Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University. His research interests include machine learning, knowledge discovery on biological data, analyzing biochemical networks and biochemical databases. As a Lane Fellow, he is currently working on algorithms to discover disease genes for Autism Spectrum Disorder and methods to improve data quality of NGS data.
DATE: 29 December, 2014, Monday @ 13:40
PLACE: EA-409