In the last decade or so, large scale gene sequencing, combinatorial biochemistry, DNA PCR and many other innovations in molecular biotechnology have transformed biology from a data-poor science to a data-rich one. This data is a harbinger of great change in medicine, in agriculture, and in our fundamental understanding of life. However, the availability of an exponentially growing onslaught of relevant data is only the first step toward understanding. There are many scientifically and economically significant opportunities (and challenges) for intelligent data analysis in exploiting this information. In this talk, I will give a brief overview of the kinds of data available and the open problems in the field, describe a few successes, and speculate about the future.
Dr. Lawrence Hunter is the director of the Machine Learning Project at the (U.S.) National Library of Medicine, and a Fellow of the Krasnow Institute of Advanced Study in Cognition at George Mason University. He received his Ph.D. in Computer Science from Yale University in 1989. He edited the MIT Press book "Artificial Intelligence and Molecular Biology," and was recently elected the founding president of the International Society for Computational Biology. His research contributions span the range from basic contributions to machine learning methodology to development of IDA technology for clinical and pharmaceutical industry applications.