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This book brings together research articles by active practitioners and leading researchers reporting recent advances in the field of knowledge discovery.

An overview of the field, looking at the issues and challenges involved is followed by coverage of recent trends in data mining. This provides the context for the subsequent chapters on methods and applications. Part I is devoted to the foundations of mining different types of complex data like trees, graphs, links and sequences. A knowledge discovery approach based on problem decomposition is also described. Part II presents important applications of advanced mining techniques to data in unconventional and complex domains, such as life sciences, world-wide web, image databases, cyber security and sensor networks.

With a good balance of introductory material on the knowledge discovery process, advanced issues and state-of-the-art tools and techniques, this book will be useful to students at Masters and PhD level in Computer Science, as well as practitioners in the field.

Contents: Introduction, Genetic Algorithms, Supervised Classification Using Genetic Algorithms, Theoretical Analysis of the GA-Classifier, Variable String Lengths in GA-Classifier, Chromosome Differentiation in VGA-Classifier, Multi-objective VGA-Classifier and Quantitative Indices, Genetic Algorithms in Clustering, Genetic Learning in Bioinformatics, Genetic Algorithms and Web Intelligence, Appendices, References, Index

This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. It examines how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries. Coverage also demonstrates the effectiveness of the genetic classifiers vis-à-vis several widely used classifiers, including neural networks. The book provides a balanced mixture of theories, algorithms, and applications, in particular results from the bioinformatics and Web intelligence domains.

Bioinformatics, a field devoted to the interpretation and analysis of biological data using computational techniques, has evolved tremendously in recent years due to the explosive growth of biological information generated by the scientific community. Soft computing is a consortium of methodologies that work synergistically and provides, in one form or another, flexible information processing capabilities for handling real-life ambiguous situations. Several research articles dealing with the application of soft computing tools to bioinformatics have been published in the recent past; however, they are scattered in different journals, conference proceedings and technical reports, thus causing inconvenience to readers, students and researchers.

This book, unique in its nature, is aimed at providing a treatise in a unified framework, with both theoretical and experimental results, describing the basic principles of soft computing and demonstrating the various ways in which they can be used for analyzing biological data in an efficient manner. Interesting research articles from eminent scientists around the world are brought together in a systematic way such that the reader will be able to understand the issues and challenges in this domain, the existing ways of tackling them, recent trends, and future directions. This book is the first of its kind to bring together two important research areas, soft computing and bioinformatics, in order to demonstrate how the tools and techniques in the former can be used for efficiently solving several problems in the latter.

Contents:

Overview:
Bioinformatics: Mining the Massive Data from High Throughput Genomics Experiments (H Tang & S Kim)
An Introduction to Soft Computing (A Konar & S Das)
Biological Sequence and Structure Analysis:
Reconstructing Phylogenies with Memetic Algorithms and Branch-and-Bound (J E Gallardo et al.)
Classification of RNA Sequences with Support Vector Machines (J T L Wang & X Wu)
Beyond String Algorithms: Protein Sequence Analysis Using Wavelet Transforms (A Krishnan & K-B Li)
Filtering Protein Surface Motifs Using Negative Instances of Active Sites Candidates (N L Shrestha & T Ohkawa)
Distill: A Machine Learning Approach to Ab Initio Protein Structure Prediction (G Pollastri et al.)
In Silico Design of Ligands Using Properties of Target Active Sites (S Bandyopadhyay et al.)
Gene Expression and Microarray Data Analysis:
Inferring Regulations in a Genomic Network from Gene Expression Profiles (N Noman & H Iba)
A Reliable Classification of Gene Clusters for Cancer Samples Using a Hybrid Multi-Objective Evolutionary Procedure (K Deb et al.)
Feature Selection for Cancer Classification Using Ant Colony Optimization and Support Vector Machines (A Gupta et al.)
Sophisticated Methods for Cancer Classification Using Microarray Data (S-B Cho & H-S Park)
Multiobjective Evolutionary Approach to Fuzzy Clustering of Microarray Data (A Mukhopadhyay et al.)

4. Ujjwal Maulik, Sanghamitra Bandyopadhyay, Jason T. Wang, Computational Intelligence and Pattern Analysis in Biology Informatics,Wiley Interscience, USA, November 2010

ISBN: 978-0-470-58159-9, Hardcover, 372 pages

An invaluable tool in Bioinformatics, this unique volume provides both theoretical and experimental results, and describes basic principles of computational intelligence and pattern analysis while deepening the reader's understanding of the ways in which these principles can be used for analyzing biological data in an efficient manner.

This book synthesizes current research in the integration of computational intelligence and pattern analysis techniques, either individually or in a hybridized manner. The purpose is to analyze biological data and enable extraction of more meaningful information and insight from it. Biological data for analysis include sequence data, secondary and tertiary structure data, and microarray data. These data types are complex and advanced methods are required, including the use of domain-specific knowledge for reducing search space, dealing with uncertainty, partial truth and imprecision, efficient linear and/or sub-linear scalability, incremental approaches to knowledge discovery, and increased level and intelligence of interactivity with human experts and decision makers

Chapters authored by leading researchers in CI in biology informatics.
Covers highly relevant topics: rational drug design; analysis of microRNAs and their involvement in human diseases.
Supplementary material included: program code and relevant data sets correspond to chapters.

5. U. Maulik, S. Bandyopadhyay and A. Mukhopadhyay, Multiobjective Genetic Algorithms for Clustering: Applications in Data Mining and Bioinformatics, Springer, Heidelberg, Germany, 2011.