Life Science Data Mining (Science, Engineering, and Biology Informatics v. 2)
By: Stephen Wong (author), Chung-Sheng Li (author)Hardback
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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.
Survey of Early Warning Systems for Environmental and Public Health Applications; Time-Lapse Cell Cycle Quantitative Data Analysis Using Gaussian Mixture Models; Diversity and Accuracy of Data Mining Ensemble; Integrated Clustering for Microarray Data; Complexity and Synchronization of EEG with Parametric Modeling; Bayesian Fusion of Syndromic Surveillance with Sensor Data for Disease Outbreak Classification; An Evaluation of Over-the-Counter Medication Sales for Syndromic Surveillance; Collaborative Health Sentinel; A Multi-Modal System Approach for Drug Abuse Research and Treatment Evaluation: Information Systems Needs and Challenges; Knowledge Representation for Versatile Hybrid Intelligent Processing Applied in Predictive Toxicology; Ensemble Classification System Implementation for Biomedical Microarray Data; An Automated Method for Cell Phase Identification in High Throughput Time-Lapse Screens; Inference of Transcriptional Regulatory Networks Based on Cancer Microarray Data; Data Mining in Biomedicine; Mining Multilevel Association Rules from Gene Ontology and Microarray Data; A Proposed Sensor-Configuration and Sensitivity Analysis of Parameters with Applications to Biosensors.
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