Life Science Data Mining

This timely book identifies and highlights the latest data mining paradigms to analyze, combine, integrate, model and simulate vast amounts of heterogeneous multi-modal, multi-scale data for emerging real-world applications in life science. The cutting-edge topics presented include bio-surveillance, disease outbreak detection, high throughput bioimaging, drug screening, predictive toxicology, biosensors, and the integration of macro-scale bio-surveillance and environmental data with micro-scale biological data for personalized medicine. This collection of works from leading researchers in the field offers readers an exceptional start in these areas.

Stephen Wong is the founding Director of the Center for Bioinformatics, Harvard Center of Neurodegeneration and Repair (HCNR) the Executive Director of Functional and Molecular Imaging Center, and an Associate Professor of Radiology, Brigham & Women's Hospital and Harvard Medical School. His research interests include biomedical informatics and imaging and the interplay between the two in solving pragmatic problems. Dr Wong is a hybrid scientist and has over 20 years of R&D experience with leading institutions in academia and industry. He has published over 170 peer-reviewed papers and holds seven patents in biomedical informatics. He has also served on NIH and NSF scientific review panels and the editorial boards of many international journals. Chung-Sheng Li is a Fellow of IEEE and a Department Head of IBM Watson Research Center, New York, USA. He is currently an associate editor for the Journal of Computer Vision and Image Understanding. His research interests include environmental and public health activity monitoring, digital libraries, security, databases, network communications, and content based retrieval of images and video. He served regularly on many NSF and other grant research panels.