Soft Computing Methods For Genomic Analysis
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SOFT COMPUTING METHODS FOR GENOMIC ANALYSIS
Author: Dr. Lohitha Lakshmi Kanchi and Dr. Lakshmi Praveena Tunuguntla
language: en
Publisher: Ashok Yakkaldevi
Release Date: 2023-03-11
Among numerous cancers, breast cancer is one type of cancer in which most tumors are formed in females' breasts and rarely in males. Cell growth remains irregular in this type of cancer, and a cancerous tumor in the breast of women develops without a gap. The increasing occurrence of breast cancer in women typically leads to the death of females. Breast Cancer may be caused due to inherited DNA or abnormal change in DNA / RNA structure. The structure and arrangement of nucleotides in genomes decide the characteristics of living organisms. During the transition from parent to child via inheritance, certain abnormal changes in the arrangement of genes take place. The search for disease incidence and control procedures are being carried out quickly, despite considerable progress in breast cancer. It is determined that one reason for the origin and spread of breast cancer in subsequent generations is also genetic. Researchers concentrate on studying cancer cell gene sequences to detect instances of similarities and unusual changes in gene structure from parent to generation of children
Analysis of Biological Data
Author: Sanghamitra Bandyopadhyay
language: en
Publisher: World Scientific
Release Date: 2007
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. Sample Chapter(s). Chapter 1: Bioinformatics: Mining the Massive Data from High Throughput Genomics Experiments (160 KB). 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.). Readership: Graduate students and researchers in computer science, bioinformatics, computational and molecular biology, artificial intelligence, data mining, machine learning, electrical engineering, system science; researchers in pharmaceutical industries.
Soft Computing Methods for Practical Environment Solutions: Techniques and Studies
"This publication presents a series of practical applications of different Soft Computing techniques to real-world problems, showing the enormous potential of these techniques in solving problems"--Provided by publisher.