Title: Protein classification via an ant-inspired association rules-based classifier

Authors: Muhammad Asif Khan; Waseem Shahzad; Abdul Rauf Baig

Addresses: Department of Computer Science, National University of Computer and Emerging Sciences (NUCES), Sector H-11/4, Islamabad, Pakistan ' Department of Computer Science, National University of Computer and Emerging Sciences (NUCES), Sector H-11/4, Islamabad, Pakistan ' Department of Computer Science, National University of Computer and Emerging Sciences (NUCES), Sector H-11/4, Islamabad, Pakistan; College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), P.O. Box 5701, Riyadh 11432, Saudi Arabia

Abstract: Association rules mining and classification rules discovery are two important data mining techniques used to expose the relations among large sets of data items. The technique aims to find out the rules that satisfy the predefined minimum support and the confidence. Association rules mining has successfully been implemented in biomedical research and has demonstrated encouraging results in analysing the gene expression data in order to discover the relevant biological association among different genes, gene expression, and various protein properties like protein functionality and sequence similarity. In this paper, we applied the association rule mining technique - the ACO-AC to the problem of classifying proteins into its correct fold of the SCOP dataset. The technique combines the association rules mining and supervised classification mechanism using ant colony optimisation. Experimental results reveal the classifier performance in protein classification problem as excellent by identifying most accurate and compact rules.

Keywords: association rules mining; rules discovery; structural classification; protein classification; ant colony optimisation; ACO; rule-based classifiers; data mining; SCOP dataset; proteins.

DOI: 10.1504/IJBIC.2016.074631

International Journal of Bio-Inspired Computation, 2016 Vol.8 No.1, pp.51 - 65

Available online: 09 Feb 2016 *

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