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.

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