Overview and Goals Data arriving in time order (a data stream) arises in fields ranging from physics to finance to medicine to music, just to name a few. Often the data comes from sensors (in physics and medicine for example) whose data rates continue to improve dramati cally as sensor technology improves. Further, the number of sensors is increasing, so correlating data between sensors becomes ever more critical in orderto distill knowl edge from the data. On-line response is desirable in many applications (e.g., to aim a telescope at a burst of activity in a galaxy or to perform magnetic...

Computers built from DNA, bacteria, or foam. Robots that fix themselves on Mars. Bridges that report when they are aging. This is the bizarre and fascinating world of Natural Computing. Computer scientist and Scientific American s Puzzling Adventures columnist Dennis Shasha here teams up with journalist Cathy Lazere to explore the outer reaches of computing. Drawing on interviews with fifteen leading scientists, the authors present an unexpected vision: the future of computing is a synthesis with nature. That vision will change not only computer science but also fields as disparate as...

Inferring gene regulatory networks is a difficult problem to solve due to the relative scarcity of data compared to the potential size of the networks. While researchers have developed techniques to find some of the underlying network structure, there is still no one-size-fits-all algorithm for every data set.

Network Inference in Molecular Biology examines the current techniques used by researchers, and provides key insights into which algorithms best fit a collection of data. Through a series of in-depth examples, the book also outlines how to mix-and-match algorithms, in...