Soft computing techniques for hub sequence analysis /

Soft computing techniques for hub sequence analysis /

Computer Science

Bioinformatics, the combination of Biology and Information Technology has become a pioneer industry booming worldwide. One of the grand challenges in biology is to understand organizing principles of bimolecular networks. There seems to be a deliberate effort towards uncovering new laws of biological complexity. One of the most pressing needs in this area is the understanding of protein-protein interaction networks and their complexity. Hub proteins- network elements with high connectivity- literally ??hold the networks together. Though several experimental methods have been developed to identify hub proteins, it is important to supplement procedures for pattern recognition to classify/predict hub protein sequences. This research work aims at the classification and prediction of hub proteins of two model organisms- Homo sapiens and Escherichia coli using different computational approaches of pattern recognition such as Principal Component Analysis (PCA), Artificial Neural Network (ANN) and Linear Discriminant Analysis using (i) Class Dependent Approach (LDACD), (ii) Class Independent Approach (LDACIND), and (iii) Normal Bayes Classification (LDANB).

S, Athmaja. - 0935005

CHRIST (Deemed to be University)

2010

PDF

English

Mphil