Pattern Recognition in Bioinformatics: International Workshop, PRIB 2006, Hong Kong, China, August 20, 2006, Proceedings / Edition 1

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This book constitutes the refereed proceedings of the International Workshop on Pattern Recognition in Bioinformatics, PRIB 2006, held in Hong Kong, within the scope of the 18th International Conference on Pattern Recognition, ICPR 2006. The book presents 19 revised full papers, covering all topics of the creation and maintenance of biological databases, and the discovery of knowledge from life sciences data. Includes an introduction to Pattern Recognition in Bioinformatics.

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Table of Contents

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.

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