Computational Prediction of Amyloidogenic Regions in Protiens : A Machine Learning Approach

Nair, Smitha Sunil Kumaran and Subbareddy , NV and Hareesha , K S (2010) Computational Prediction of Amyloidogenic Regions in Protiens : A Machine Learning Approach. In: Proc. 9th Annual International Conference on Computational Systems Bioinformatics (ISB 2010), Stanford University, U. S,, 16-18.

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Abstract

Amyloidogenic regions in polypeptide chains are associated with a number of pathologies including neurodegenerative diseases. Recent studies have shown that small regions of proteins are responsible for its amyloidogenic behavior. Therefore, identifying these short peptides is critical for understanding diseases associated with protein aggregation. Owing to the limitations of molecular techniques for the identification of fibril forming targets, it became apparent that clever computational techniques might enable their discovery in silico. We propose a machine learning based method to predict the amyloid fibril-forming short stretches of peptides using Support Vector Machine. The features of this method are based on the physicochemical properties of amino acids. Inorder to get an optimal number of properties, a feature selection approach based on Genetic Algorithm is PErformed. The presented algorithm achieved a balanced prediction performance in terms of true positive and false positive rates in predicting a peptide status: amyloidogenic or non-amyloidogenic, which is not reflected in the existing methods.

Item Type: Conference or Workshop Item (Paper)
Subjects: Engineering > MIT Manipal > Computer Science and Engineering
Depositing User: MIT Library
Date Deposited: 14 Jun 2011 10:42
Last Modified: 25 Mar 2013 04:39
URI: http://eprints.manipal.edu/id/eprint/255

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