An efficient computational intelligence technique for classification of protein sequences

Many artificial intelligence techniques have been developed to process the constantly increasing volume of data to extract meaningful information from it. The accurate annotation of the unknown protein using the classification of the protein sequence into an existing superfamily is considered a crit...

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Main Authors: Iqbal, M.J., Faye, I., Said, A.M., Samir, B.B.
Format: Conference or Workshop Item
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.31229 /
Published: Institute of Electrical and Electronics Engineers Inc. 2014
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938777193&doi=10.1109%2fICCOINS.2014.6868352&partnerID=40&md5=2676071139cc5d5754f5f9bfa3ea6f99
http://eprints.utp.edu.my/31229/
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id utp-eprints.31229
recordtype eprints
spelling utp-eprints.312292022-03-25T09:03:33Z An efficient computational intelligence technique for classification of protein sequences Iqbal, M.J. Faye, I. Said, A.M. Samir, B.B. Many artificial intelligence techniques have been developed to process the constantly increasing volume of data to extract meaningful information from it. The accurate annotation of the unknown protein using the classification of the protein sequence into an existing superfamily is considered a critical and challenging task in bioinformatics and computational biology. This classification would be helpful in the analysis and modeling of unknown protein to determine their structure and function. In this paper, a frequency-based feature encoding technique has been used in the proposed framework to represent amino acids of a protein's primary sequence. The technique has considered the occurrence frequency of each amino acid in a sequence. Popular classification algorithms such as decision tree, naive Bayes, neural network, random forest and support vector machine have been employed to evaluate the effectiveness of the encoding method utilized in the proposed framework. Results have indicated that the decision tree classifier significantly shows better results in terms of classification accuracy, specificity, sensitivity, F-measure, etc. The classification accuracy of 88.7 was achieved over the Yeast protein sequence data taken from the well-known UniProtKB database. © 2014 IEEE. Institute of Electrical and Electronics Engineers Inc. 2014 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938777193&doi=10.1109%2fICCOINS.2014.6868352&partnerID=40&md5=2676071139cc5d5754f5f9bfa3ea6f99 Iqbal, M.J. and Faye, I. and Said, A.M. and Samir, B.B. (2014) An efficient computational intelligence technique for classification of protein sequences. In: UNSPECIFIED. http://eprints.utp.edu.my/31229/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Many artificial intelligence techniques have been developed to process the constantly increasing volume of data to extract meaningful information from it. The accurate annotation of the unknown protein using the classification of the protein sequence into an existing superfamily is considered a critical and challenging task in bioinformatics and computational biology. This classification would be helpful in the analysis and modeling of unknown protein to determine their structure and function. In this paper, a frequency-based feature encoding technique has been used in the proposed framework to represent amino acids of a protein's primary sequence. The technique has considered the occurrence frequency of each amino acid in a sequence. Popular classification algorithms such as decision tree, naive Bayes, neural network, random forest and support vector machine have been employed to evaluate the effectiveness of the encoding method utilized in the proposed framework. Results have indicated that the decision tree classifier significantly shows better results in terms of classification accuracy, specificity, sensitivity, F-measure, etc. The classification accuracy of 88.7 was achieved over the Yeast protein sequence data taken from the well-known UniProtKB database. © 2014 IEEE.
format Conference or Workshop Item
author Iqbal, M.J.
Faye, I.
Said, A.M.
Samir, B.B.
spellingShingle Iqbal, M.J.
Faye, I.
Said, A.M.
Samir, B.B.
An efficient computational intelligence technique for classification of protein sequences
author_sort Iqbal, M.J.
title An efficient computational intelligence technique for classification of protein sequences
title_short An efficient computational intelligence technique for classification of protein sequences
title_full An efficient computational intelligence technique for classification of protein sequences
title_fullStr An efficient computational intelligence technique for classification of protein sequences
title_full_unstemmed An efficient computational intelligence technique for classification of protein sequences
title_sort efficient computational intelligence technique for classification of protein sequences
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2014
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938777193&doi=10.1109%2fICCOINS.2014.6868352&partnerID=40&md5=2676071139cc5d5754f5f9bfa3ea6f99
http://eprints.utp.edu.my/31229/
_version_ 1741197539196010496
score 11.62408

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