Computational Technique for an Efficient Classification of Protein Sequences With Distance‐Based Sequence Encoding Algorithm. (21st September 2015)

• Record Type: Journal Article
• Authors: Iqbal, Muhammad Javed; Faye, Ibrahima; Said, Abas MD; Samir, Brahim Belhaouari
• Is Part Of: Computational intelligence. Volume 33:Number 1(2017)
• Page Start: 32
• Page End: 55
• Publication Date: 2015-09-21
• Subjects: sequence encoding, biological data mining, feature selection, superfamily, protein classification algorithm; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12069
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract : Machine learning is being implemented in bioinformatics and computational biology to solve challenging problems emerged in the analysis and modeling of biological data such as DNA, RNA, and protein. The major problems in classifying protein sequences into existing families/superfamilies are the following: the selection of a suitable sequence encoding method, the extraction of an optimized subset of features that possesses significant discriminatory information, and the adaptation of an appropriate learning algorithm that classifies protein sequences with higher classification accuracy. The accurate classification of protein sequence would be helpful in determining the structure and function of novel protein sequences. In this article, we have proposed a distance‐based sequence encoding algorithm that captures the sequence's statistical characteristics along with amino acids sequence order information. A statistical metric‐based feature selection algorithm is then adopted to identify the reduced set of features to represent the original feature space. The performance of the proposed technique is validated using some of the best performing classifiers implemented previously for protein sequence classification. An average classification accuracy of 92% was achieved on the yeast protein sequence data set downloaded from the benchmark UniProtKB database.