Clustering Challenges in Biological Networks

The first five chapters in this collection survey fixed parameter algorithms for graph-modeled data clustering, the probabilistic distance clustering algorithm, analysis of regulatory and interaction networks, graph-based approaches to motif discovery, and statistical clustering analysis. The remaining 11 papers present new research on critical node detection, diversity graphs, the complexity of feature selection, subjective and objective pharmacovigilance association measures, EEG recordings of mesial temporal lobe epilepsy, and projected clustering. The editors are affiliated with major U.S. universities, and some of the papers were presented at a June 2006 DIMACS workshop held at Rutgers University. Annotation c2010 Book News, Inc., Portland, OR (booknews.com)