Life Science Data Mining

Life Science Data Mining

eBook - 2006
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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. Sample Chapter(s). Chapter 1: Survey of Early Warning Systems for Environmental and Public Health Applications (211 KB). Contents: 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. Readership: Graduate students, academics, and engineering and IT professionals.
Publisher: Singapore : World Scientific Publishing Company, 2006
Copyright Date: ©2006
ISBN: 9789812772664
Branch Call Number: Electronic book
Characteristics: 1 online resource (388 pages)
Additional Contributors: Li, Chung-Sheng
ProQuest (Firm)


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