Iqbal, M.J. and Faye, I. and Said, A.M.D. and Samir, B.B. (2017) Computational Technique for an Efficient Classification of Protein Sequences With Distance-Based Sequence Encoding Algorithm. Computational Intelligence, 33 (1). pp. 32-55. ISSN 08247935
Full text not available from this repository.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. © 2015 Wiley Periodicals, Inc.
Item Type: | Article |
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Additional Information: | cited By 2 |
Uncontrolled Keywords: | Algorithms; Artificial intelligence; Bioinformatics; Computational efficiency; Data mining; Encoding (symbols); Feature extraction; Learning algorithms; Learning systems; Nucleic acids; Proteins, Analysis and modeling; Biological data mining; Classification accuracy; Computational biology; Computational technique; Feature selection algorithm; Protein sequence classification; Statistical characteristics, Classification (of information) |
Depositing User: | Mr Ahmad Suhairi UTP |
Date Deposited: | 09 Nov 2023 16:20 |
Last Modified: | 09 Nov 2023 16:20 |
URI: | https://khub.utp.edu.my/scholars/id/eprint/8899 |