Mem-PHybrid: hybrid features-based prediction system for classifying membrane protein types

Analytical Biochemistry
Maqsood Hayat, Asifullah Khan

Abstract

Membrane proteins are a major class of proteins and encoded by approximately 20% to 30% of genes in most organisms. In this work, a two-layer novel membrane protein prediction system, called Mem-PHybrid, is proposed. It is able to first identify the protein query as a membrane or nonmembrane protein. In the second level, it further identifies the type of membrane protein. The proposed Mem-PHybrid prediction system is based on hybrid features, whereby a fusion of both the physicochemical and split amino acid composition-based features is performed. This enables the proposed Mem-PHybrid to exploit the discrimination capabilities of both types of feature extraction strategy. In addition, minimum redundancy and maximum relevance has also been applied to reduce the dimensionality of a feature vector. We employ random forest, evidence-theoretic K-nearest neighbor, and support vector machine (SVM) as classifiers and analyze their performance on two datasets. SVM using hybrid features yields the highest accuracy of 89.6% and 97.3% on dataset1 and 91.5% and 95.5% on dataset2 for jackknife and independent dataset tests, respectively. The enhanced prediction performance of Mem-PHybrid is largely attributed to the exploitation of the discr...Continue Reading

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Citations

Apr 5, 2016·Computer Methods and Programs in Biomedicine·Muhammad Iqbal, Maqsood Hayat
Aug 12, 2014·The Journal of Membrane Biology·Hong-Liang Zou
Nov 21, 2016·The Journal of Membrane Biology·Ahmad Hassan ButtYaser Daanial Khan

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