Computational Intelligence Methods for Bioinformatics and Biostatistics

<p>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. </p>