| Title | Pattern Recognition Methods for Biomolecular Data Analysis |
| Speaker | Prof. Hong Yan |
| Chair | Jung-Hsien Chiang |
Abstract
Biomolecules, such as DNA and proteins, are extremely “intelligent”. They can perform sophisticated molecular recognition tasks, which no existing computer based pattern recognition methods can match. Analysis of biomolecular data is indispensable for understanding such intelligence, the underlying biological structures and processes, and biological lives in general. In this talk, I’ll provide an introduction to different types of biomolecular data, and present our work on gene expression data analysis and prediction of biomolecular interactions using pattern classification algorithms. I’ll discuss a geometric biclustering method our group has developed, which can be used to detect coherent patterns in terms of subsets of genes and subsets of conditions. An interesting link between geometric biclustering and the spectral graph theory will also be demonstrated. The method can be applied to disease diagnosis and drug therapeutic effect assessment based on gene expression data. I’ll also discuss a 3D pattern matching technique for identifying potential hydrogen bonds from all possible donor and acceptor atoms in two biomolecules. In this algorithm, compatibility measures developed in pattern recognition can be used to formulate cooperativity in biological systems mathematically. This method has been applied to the prediction of interactions between proteins and drug molecules and the analysis of drug resistance.
Biography
Hong Yan received his Ph.D. degree from Yale University. He was Professor of Imaging Science at the University of Sydney and is currently Professor of Computer Engineering at City University of Hong Kong. His research interests include image processing, pattern recognition and bioinformatics, and he has over 300 journal and conference publications in these areas. Professor Yan was elected an IAPR fellow for contributions to document image analysis and an IEEE fellow for contributions to image recognition techniques and applications.