Machine Learning in Life Sciences
Explosion of experimental data generated by breakthrough technologies in life sciences (such as sequencing, genotyping, gene and protein expression profiling) has created ample opportunities for discovery by way of data analysis. In this talk, I will highlight areas of molecular biology and clinical diagnostics which can well be served by methods and tools developed in machine learning and multivariate statistics. I will also point out similarities and fundamental differences between statistical and machine learning approaches to these problems. Specific examples of applying machine learning to cancer classification, prediction of drug response and population genetics will be presented.
Ljubomir Buturovic received his Ph.D. degree from the School of Electrical Engineering, University of Belgrade, Serbia. He is currently Adjunct Professor of Computer Science at San Francisco State University, and Director of Microarray Data Analysis at Pathwork Diagnostics in San Jose, California. Prior to joining SFSU and Pathwork Diagnostics he was Bioinformatics Director at Incyte Corporation. His research interests include applications of machine learning in life sciences, with emphasis on clinical diagnostics and gene mapping of complex traits.