Computational Intelligence Methods for Bioinformatics and Biostatistics 10th International Meeting, CIBB 2013, Nice, France, June 20-22, 2013, Revised Selected Papers

This book constitutes the thoroughly refereed post-conference
proceedings of the 10th International Meeting on Computational
Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2013, held in Nice, France in June 2013.
The 19 revised full papers presented were carefully reviewed and
selected from 35 submissions. The papers are organized in topical
sections on bioinformatics, biostatistics, knowledge based medicine, and data integration and analysis in omic-science.

Dynamic Gaussian Graphical Models for Modelling Genomic Networks.- Molecular Docking for Drug Discovery: Machine-Learning Approaches for Native Pose Prediction of Protein-Ligand Complexes.- BioCloud Search EnGene: Surfing Biological Data on the Cloud.- Genomic Sequence Classification Using Probabilistic Topic Modeling.- Community Detection in Protein-Protein Interaction Networks Using Spectral and Graph Approaches.- Weighting Scheme Methods for Enhanced Genomic Annotation Prediction.- French Flag Tracking by Morphogenetic Simulation Under Developmental Constraints.- High–Dimensional Sparse Matched Case–Control and Case–Crossover Data: A Review of Recent Works, Description of an R Tool and an Illustration of the Use in Epidemiological Studies.- Piecewise Exponential Artificial Neural Networks (PEANN) for Modeling Hazard Function with Right Censored Data.- Writing Generation Model for Health Care Neuromuscular System Investigation.- Clusters Identification in Binary Genomic Data: The Alternative Offered by Scan Statistics Approach.- Reverse Engineering Methodology for Bioinformatics Based on Genetic Programming, Differential Expression Analysis and Other Statistical Methods.- Integration of Clinico-Pathological and microRNA Data for Intelligent Breast Cancer Relapse Prediction Systems.- Superresolution MUSIC Based on Marcenko-Pastur Limit Distribution Reduces Uncertainty and Improves DNA Gene Expression-Based Microarray Classification.- Prediction of Single-Nucleotide Polymorphisms Causative of Rare Diseases.- A Framework for Mining Life Sciences Data on the Semantic Web in an Interactive, Graph-Based Environment.- Combining Not-Proper ROC Curves and Hierarchical Clustering to Detect Differentially Expressed Genes in Microarray Experiments.- Fast and Parallel Algorithm for Population-Based Segmentation of Copy-Number Profiles.- Identification of Pathway Signatures in Parkinson’s Disease with Gene Ontology and Sparse Regularization.