T. Can, Recent Advances in Life Sciences and Computational Challenges

Recent Advances in Life Sciences and Computational Challenges

Tolga Can

Abstract:
We are witnessing a deluge of new biological data from different high-throughput pipelines (genomic, proteomics, microarrays, bio-images). There is a need for new computational methods for complete biological understanding of the data in a scalable manner. The current realization is that a single data source is seldom sufficient; therefore, methods that can integrate data from multiple heterogeneous sources should be developed. Working with biological data is challenging, because the data is often very noisy, incomplete, heterogeneous, and biased. In this talk, I will present some of the methods we have developed to address these problems. First, I will describe two methods, CTSS and ProGreSS, that
we have developed for efficiently querying protein sequences and structures. Then, I will present a decision tree based ensemble classifier for automatically classifying protein structures with high accuracy. I will then give another solution to the same problem by
utilizing the global view of similarity relations between proteins. I will conclude with a discussion of how similar techniques can be used to solve different problems in systems biology, such as prediction of protein-protein interactions and prediction of new members of a core complex.

Biography:
Dr. Tolga Can received his Bachelor's degree in Computer Engineering from Middle East Technical University (METU), Ankara, Turkey in 1998 and his Master's (2003) and Ph.D. (2004) degrees in Computer Science from University of California at Santa Barbara. He is currently a postdoctoral researcher at the Center for Bio-Image Informatics at UCSB.
His research interests are computer graphics (visualization and modeling of protein structures), bioinformatics (protein structure analysis, construction and analysis of genome wide protein networks), and statistical modeling.

November 8, 2005, 09:40, FENS 2019