eprintid: 4260
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/42/60
datestamp: 2023-11-09 16:15:55
lastmod: 2023-11-09 16:15:55
status_changed: 2023-11-09 15:58:02
type: conference_item
metadata_visibility: show
creators_name: Iqbal, M.J.
creators_name: Faye, I.
creators_name: Said, A.M.
creators_name: Samir, B.B.
title: An efficient computational intelligence technique for classification of protein sequences
ispublished: pub
keywords: Amino acids; Bioinformatics; Computer aided diagnosis; Data mining; Decision trees; Encoding (symbols); Intelligent computing; Proteins; Signal encoding; Support vector machines, Analysis and modeling; Artificial intelligence techniques; Classification accuracy; Classification algorithm; Computational intelligence techniques; Decision tree classifiers; Protein Classification; Superfamily, Classification (of information)
note: cited By 0; Conference of 2014 International Conference on Computer and Information Sciences, ICCOINS 2014 ; Conference Date: 3 June 2014 Through 5 June 2014; Conference Code:112912
abstract: 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.
date: 2014
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938777193&doi=10.1109%2fICCOINS.2014.6868352&partnerID=40&md5=2676071139cc5d5754f5f9bfa3ea6f99
id_number: 10.1109/ICCOINS.2014.6868352
full_text_status: none
publication: 2014 International Conference on Computer and Information Sciences, ICCOINS 2014 - A Conference of World Engineering, Science and Technology Congress, ESTCON 2014 - Proceedings
refereed: TRUE
isbn: 9781479943913
citation:   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.