Scalable Pattern Recognition Algorithms

Applications in Computational Biology and Bioinformatics
2014
ISBN: 978-3-319-05629-6
Verlag: Springer

Buch, Englisch, 304 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 6746 g

Recent advances in high-throughput technologies have resulted in a deluge of biological information. Yet the storage, analysis, and interpretation of such multifaceted data require effective and efficient computational tools.

This unique text/reference 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 book reviews both established and cutting-edge research, following a clear structure reflecting the major phases of a pattern recognition system: classification, feature selection, and clustering. The text provides a careful balance of theory, algorithms, and applications, with a particular emphasis given to applications in computational biology and bioinformatics.

Topics and features: reviews the development of scalable pattern recognition algorithms for computational biology and bioinformatics; 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.

This important work will be of great use to graduate students and researchers in the fields of computer science, electrical and biomedical engineering. Researchers and practitioners involved in pattern recognition, machine learning, computational biology and bioinformatics, data mining, and soft computing will also find the book invaluable.

Zielgruppe

Research

Autoren/Hrsg.

Maji, Pradipta

Paul, Sushmita

Fachgebiete

• Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik

Inhaltsverzeichnis

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

Über Autor(innen)

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.