Effective framework for protein structure prediction
by Nagamma Patil; Durga Toshniwal; Kumkum Garg
International Journal of Functional Informatics and Personalised Medicine (IJFIPM), Vol. 4, No. 1, 2012

Abstract: This paper presents a computational system to predict protein structure using N-grams and a wrapper feature selection framework (the N-gram is a subsequence composed of N characters, extracted from a larger sequence). N-gram features are extracted from a dataset consisting of 277 domains: 70 all-α domains, 61 all-β domains, 81 α/β domains and 65 α + β domains. A wrapper feature selection system, GA-SVM, is applied to obtain an optimised feature set. Using the optimised 3070-feature subset, a classifier model is trained and tested in the Support Vector Machine (SVM) learning system. This model achieves an overall accuracy of 88.09%, evaluated by a 10-fold cross-validation test. This value is 4.7% higher than the one using the initial 6,414 features. Experimental results also illustrate that employing a feature subset selection, by using the proposed GA-SVM wrapper approach, has enhanced classification accuracy in comparison to other GA-based wrapper approaches and existing protein sequence encoding methods.

Online publication date: Tue, 20-Nov-2012

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