Application of Signal Processing and Soft Computing To Genomics

Abstract

A major challenge for genomic research is to establish a relationship among sequences,structures and function of genes. In addition processing and analyzing this information are of prime importance. Basically genes are repositories for protein coding information and proteins in turn are responsible for most of the important biological functions in all cells. These in turn gives rise to analysis of DNA sequences in proteins, designing of various drugs for genetic diseases. This thesis deals with the applications of signal processing and soft computing algorithms to the field of genomics and proteinomics. Diseases like SARS and Migraine have been modeled using these tools and potential druggable compounds have been proposed which are better than the previous available drugs. Protein structural classes have been identified more accurately based on Genetic Algorithm and Particle Swarm Optimization.Better and efficient methods like Sliding-DFT and Adaptive AR Modeling were proposed to identify Protein coding regions in genes. The proposed methods showed better results as compared to existing methods.