Beschreibung:
Identifies and highlights the 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. This book presents topics which include bio-surveillance, disease outbreak detection, high throughput bioimaging and drug screening.
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.