Pattern Recognition in Bioinformatics

This book constitutes the refereed proceedings of the 8th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2014, held in Stockholm, Sweden in August 2014. The 9 revised full papers and 9 revised short papers presented were carefully reviewed and selected from 29 submissions. The focus of the conference was on the latest Research in Pattern Recognition and Computational Intelligence-Based Techniques Applied to Problems in Bioinformatics and Computational Biology.

Inhalt

FULL PAPERS.- Acquiring Decision Rules for Predicting Ames-Negative Hepatocarcinogens Using Chemical-Chemical Interactions.- Using Topology Information for Protein-Protein Interaction Prediction.- Biases of drug{target interaction network data.- Logol: Expressive Pattern Matching in sequences Application to Ribosomal Frameshift Modeling.- Evolutionary Algorithm based on New Crossover for the Biclustering of Gene Expression Data.- SFFS-SW: A feature selection algorithm exploring the small-world properties of GNs.- CytomicsDB: A Metadata-based storage and retrieval approach for High-Throughput Screening Experiments.- CUDAGRN: Parallel Speedup of Inferring Large Gene Regulatory.- Networks from Expression Data Using Random Forest.- SHORT ABSTRACTS.- Analysis of miRNA expression profiles in breast cancer using biclustering.- Gram-positive and Gram-negative Subcellular Localization Using Rotation Forest and Physicochemical-based Features.- Data Driven Feature Selection for RNA-Seq Differential Expression Analysis.- Intramuscular fat percentage estimation through ultrasound images.- An integrated approach of gene expression and DNA-methylation profiles of WNT signaling genes uncovers novel prognostic markers in Acute Myeloid Leukemia.- Improving performance of the eXtasy model by hierarchical sampling.- Popovic et al.Ensemble Neural Networks Scoring Functions for Accurate Binding Affinity.- Prediction of Protein-Ligand Complexes.- Integration of Gene Expression and DNA-methylation Profiles Improves Molecular Subtype Classification in Acute Myeloid Leukemia.- The Relative Vertex-to-Vertex Clustering Value- A New Criterion for the Fast Detection of Functional Modules in Protein Interaction Networks.