Computational Intelligence Methods for Bioinformatics and Biostatistics

Chapter 1 Dynamic Gaussian Graphical Models for Modelling Genomic Networks

Chapter 2 Molecular Docking for Drug Discovery: Machine-Learning Approaches for Native Pose Prediction of Protein-Ligand Complexes

Chapter 3 BioCloud Search EnGene: Surfing Biological Data on the Cloud

Chapter 4 Genomic Sequence Classification Using Probabilistic Topic Modeling

Chapter 5 Community Detection in Protein-Protein Interaction Networks Using Spectral and Graph Approaches

Chapter 6 Weighting Scheme Methods for Enhanced Genomic Annotation Prediction

Chapter 7 French Flag Tracking by Morphogenetic Simulation Under Developmental Constraints

Chapter 8 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

Chapter 9 Piecewise Exponential Artificial Neural Networks (PEANN) for Modeling Hazard Function with Right Censored Data

Chapter 10 Writing Generation Model for Health Care Neuromuscular System Investigation

Chapter 11 Clusters Identification in Binary Genomic Data: The Alternative Offered by Scan Statistics Approach

Chapter 12 Reverse Engineering Methodology for Bioinformatics Based on Genetic Programming, Differential Expression Analysis and Other Statistical Methods

Chapter 13 Integration of Clinico-Pathological and microRNA Data for Intelligent Breast Cancer Relapse Prediction Systems

Chapter 14 Computational Intelligence Methods for Bioinformatics and Biostatistics

Chapter 15 Prediction of Single-Nucleotide Polymorphisms Causative of Rare Diseases

Chapter 16 A Framework for Mining Life Sciences Data on the Semantic Web in an Interactive, Graph-Based Environment

Chapter 17 Combining Not-Proper ROC Curves and Hierarchical Clustering to Detect Differentially Expressed Genes in Microarray Experiments

Chapter 18 Fast and Parallel Algorithm for Population-Based Segmentation of Copy-Number Profiles

Chapter 19 Identification of Pathway Signatures in Parkinson’s Disease with Gene Ontology and Sparse Regularization