Analysis Of Biological Data: A Soft Computing Approach
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.
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active sites candidates allergen amino acids analysis Ant Colony Optimization ants applied approach bi-gram Bioinformatics Biol biological cancer dataset chromosome clustering colon contact density contact maps crossover database defined distance evaluated evolutionary algorithm Evolutionary Computation expression levels expression profiles extracted feature selection feature subset feature vector feature-classifier filtering function fuzzy logic gene expression data gene subset genetic algorithm genome heuristic hydrophobic inference input interaction K-medoids kernel Leukemia ligand LVGALD lymphoma machine learning method microarray data mismatches molecules multiobjective multiple mutation negative instances neural networks node non-coding RNA obtained optimization parameters patterns performance pheromone prediction strength problem procedure proposed protein sequences protein structure recurring substrings represents residues RNA sequence samples secondary structure sequence alignment similar soft computing solutions structure prediction SUMOMO support vector machines surface motifs Table techniques threshold topology values wavelet transforms