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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.
List of 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.
About the author
Dr. Pradipta Maji is an Associate Professor in the Machine Intelligence Unit at the Indian Statistical Institute, Kolkata, India.
Dr. Sushmita Paul is a Research Associate at the same institution.
Summary
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.
Additional text
From the book reviews:
“This book provides unique insights into how various soft computing and machine learning methods can be formulated and used in building efficient pattern recognition models. … This is a great resource to students and researchers in the fields of computer science, electrical and biomedical engineering. The author has explained the complex ideas through numerous examples which make conceptualization easy. … The well-organized chapters as well as use of different notations and typescripts make it a user-friendly reference.” (Parthiv Amin, Doody’s Book Reviews, August, 2014)
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From the book reviews:
"This book provides unique insights into how various soft computing and machine learning methods can be formulated and used in building efficient pattern recognition models. ... This is a great resource to students and researchers in the fields of computer science, electrical and biomedical engineering. The author has explained the complex ideas through numerous examples which make conceptualization easy. ... The well-organized chapters as well as use of different notations and typescripts make it a user-friendly reference." (Parthiv Amin, Doody's Book Reviews, August, 2014)
