Pattern Recognition in Bioinformatics: International Workshop, PRIB 2006, Hong Kong, China, August 20, 2006, Proceedings (Paperback)

Pattern Recognition in Bioinformatics: An Introduction.- Pattern Recognition in Bioinformatics: An Introduction.- 1: Signal and Motif Detection; Gene Selection.- Machine Learning Prediction of Amino Acid Patterns in Protein N-myristoylation.- A Profile HMM for Recognition of Hormone Response Elements.- Graphical Approach to Weak Motif Recognition in Noisy Data Sets.- Comparative Gene Prediction Based on Gene Structure Conservation.- Computational Identification of Short Initial Exons.- Pareto-Gamma Statistic Reveals Global Rescaling in Transcriptomes of Low and High Aggressive Breast Cancer Phenotypes.- Investigating the Class-Specific Relevance of Predictor Sets Obtained from DDP-Based Feature Selection Technique.- A New Maximum-Relevance Criterion for Significant Gene Selection.- 2: Models of DNA, RNA, and Protein Structures.- Spectral Graph Partitioning Analysis of In Vitro Synthesized RNA Structural Folding.- Predicting Secondary Structure of All-Helical Proteins Using Hidden Markov Support Vector Machines.- Prediction of Protein Subcellular Localizations Using Moment Descriptors and Support Vector Machine.- Using Permutation Patterns for Content-Based Phylogeny.- 3: Biological Databases and Imaging.- The Immune Epitope Database and Analysis Resource.- Intelligent Extraction Versus Advanced Query: Recognize Transcription Factors from Databases.- Incremental Maintenance of Biological Databases Using Association Rule Mining.- Blind Separation of Multichannel Biomedical Image Patterns by Non-negative Least-Correlated Component Analysis.- Image and Fractal Information Processing for Large-Scale Chemoinformatics, Genomics Analyses and Pattern Discovery.- Hybridization of Independent Component Analysis, Rough Sets, and Multi-Objective Evolutionary Algorithms for Classificatory Decomposition of Cortical Evoked Potentials.