Main description:

This book addresses the need for a unified framework describing how soft computing and machine learning techniques can be judiciously formulated and used in building efficient pattern recognition models. The text reviews both established and cutting-edge research, providing a careful balance of theory, algorithms, and applications, with a particular emphasis given to applications in computational biology and bioinformatics. Features: integrates different soft computing and machine learning methodologies with pattern recognition tasks; discusses in detail the integration of different techniques for handling uncertainties in decision-making and efficiently mining large biological datasets; presents a particular emphasis on real-life applications, such as microarray expression datasets and magnetic resonance images; includes numerous examples and experimental results to support the theoretical concepts described; concludes each chapter with directions for future research and a comprehensive bibliography.

Contents:

Introduction to Pattern Recognition and Bioinformatics

Part I Classification

Neural Network Tree for Identification of Splice Junction and Protein Coding Region in DNA

Design of String Kernel to Predict Protein Functional Sites Using Kernel-Based Classifiers

Part II Feature Selection

Rough Sets for Selection of Molecular Descriptors to Predict Biological Activity of Molecules

f -Information Measures for Selection of Discriminative Genes from Microarray Data

Identification of Disease Genes Using Gene Expression and Protein-Protein Interaction Data

Rough Sets for Insilico Identification of Differentially Expressed miRNAs

Part III Clustering

Grouping Functionally Similar Genes from Microarray Data Using Rough-Fuzzy Clustering

Mutual Information Based Supervised Attribute Clustering for Microarray Sample Classification

Possibilistic Biclustering for Discovering Value-Coherent Overlapping d -Biclusters

Fuzzy Measures and Weighted Co-Occurrence Matrix for Segmentation of Brain MR Images