Soft computing techniques for hub sequence analysis /

Title

Soft computing techniques for hub sequence analysis /

Creator

S, Athmaja. - 0935005

Description

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).

Date

2010-03-19

Publisher

CHRIST (Deemed to be University)

Subject

Computer Science

Format

PDF

Language

English

Type

Mphil