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.

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.