The CSE Colloquium Series is proud to present faculty candidate Jong Cheol Jeong, who will give a talk on "Measuring Functional Similarity Between Gene Products" on April 2 at 4 p.m. in Avery 115. Jeong is currently a PhD candidate in the Bioengineering Department at the University of Kansas.
Abstract
1.2 billion users in facebook, 17 million articles in Wikipedia, and 190 million tweets per day have demanded significant increase of information processing through Internet in recent years. Similarly life sciences and bioinformatics also have faced issues of processing Big data due to the explosion of publicly available genomic information resulted from the Human Genome Project (HGP) and the increasing usage of high throughput technology. HGP was completed in 2003 and resulted in identifying 20,000-25,000 genes in human DNA and determining the sequences of three billion human base pairs. The information requires huge amount of data storage and becomes difficult to process using on-hand database management tools or traditional data processing applications. Through the talk, I will present new method, Biological and Statistical Mean (BSM) score to calculate functional similarity between gene products (GPs) that can help to extract biologically relevant and statistically robust information from large-scale biomedical, genomic and proteomic data sources. BSM score is defined by 16 different scoring matrices derived from principles of multi-view learning in machine learning algorithm and five different databases including Gene Ontology, UniProt, SCOP, CATH, and KUPS. In this talk, I will show how diverse databases and principles in machine learning theory can be integrated into a simple scoring function and also present how the simple concept can give significant impact on the studies in biomedical and human life sciences. As a part of potential applications handling Big data in medical domain, I will introduce similarity-based drug target identification and disease networks using BSM scores. Application of BSM score is freely available through http://www.ittc.ku.edu/chenlab/goal/
Biography
Jong ia a PhD candidate in the Bioengineering Department at University of Kansas, Lawrence. He has two M.S. degrees from Computer Science and Computer Engineering received from University of Kansas and Chonnam National University. He has published several papers in leading journals and conferences including ICML, Bioinformatics, Nucleic Acid Research, and IEEE/ACM Transactions on Computational Biology and Bioinformatics. One of his papers has been cited more than 50 times referred as the basic standard evaluation criteria of identifying interface residues in protein binding and imbalanced data training. He has received best research paper award from Korea Fuzzy Logic and Intelligent System 2001 and awarded Wallace S. Strobel Scholarship from University of Kansas in 2008. He now is working at Bioinformatics Center in University of Kansas as a founder and developer of open source project, ST-analyzer, which is an intranet-based standalone application to analyze Molecular Dynamics simulation trajectories and expected to be installed in TeraGrid.