Data mining of protein sequences with amino acid position-based feature encoding technique

Biological data mining has been emerging as a new area of research by incorporating artificial intelligence and biology techniques for automatic analysis of biological sequence data. The size of the biological data collected under the Human Genome Project is growing exponentially. The available data...

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Main Authors: Iqbal, M.J., Faye, I., Md Said, A., Samir, B.B.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.31712 /
Published: Springer Verlag 2014
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958554492&doi=10.1007%2f978-981-4585-18-7_14&partnerID=40&md5=ae7e119e8dce09e407f1d3801dd87933
http://eprints.utp.edu.my/31712/
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spelling utp-eprints.317122022-03-29T03:35:45Z Data mining of protein sequences with amino acid position-based feature encoding technique Iqbal, M.J. Faye, I. Md Said, A. Samir, B.B. Biological data mining has been emerging as a new area of research by incorporating artificial intelligence and biology techniques for automatic analysis of biological sequence data. The size of the biological data collected under the Human Genome Project is growing exponentially. The available data is comprised of DNA, RNA and protein sequences. Automatic classification of protein sequences into different groups might be utilized to infer the structure, function and evolutionary information of an unknown protein sequence. The accurate classification of protein sequences into family/superfamily based on the primary sequence is a very complex and open problem. In this paper, an amino acid position-based feature encoding technique is proposed to represent a protein sequence using a fixed length numeric feature vector. The classification results indicate that the proposed encoding technique with a decision tree classification algorithm has achieved 85.9 classification accuracy over the Yeast protein sequence dataset. © Springer Science+Business Media Singapore 2014. Springer Verlag 2014 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958554492&doi=10.1007%2f978-981-4585-18-7_14&partnerID=40&md5=ae7e119e8dce09e407f1d3801dd87933 Iqbal, M.J. and Faye, I. and Md Said, A. and Samir, B.B. (2014) Data mining of protein sequences with amino acid position-based feature encoding technique. Lecture Notes in Electrical Engineering, 285 LN . pp. 119-126. http://eprints.utp.edu.my/31712/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Biological data mining has been emerging as a new area of research by incorporating artificial intelligence and biology techniques for automatic analysis of biological sequence data. The size of the biological data collected under the Human Genome Project is growing exponentially. The available data is comprised of DNA, RNA and protein sequences. Automatic classification of protein sequences into different groups might be utilized to infer the structure, function and evolutionary information of an unknown protein sequence. The accurate classification of protein sequences into family/superfamily based on the primary sequence is a very complex and open problem. In this paper, an amino acid position-based feature encoding technique is proposed to represent a protein sequence using a fixed length numeric feature vector. The classification results indicate that the proposed encoding technique with a decision tree classification algorithm has achieved 85.9 classification accuracy over the Yeast protein sequence dataset. © Springer Science+Business Media Singapore 2014.
format Article
author Iqbal, M.J.
Faye, I.
Md Said, A.
Samir, B.B.
spellingShingle Iqbal, M.J.
Faye, I.
Md Said, A.
Samir, B.B.
Data mining of protein sequences with amino acid position-based feature encoding technique
author_sort Iqbal, M.J.
title Data mining of protein sequences with amino acid position-based feature encoding technique
title_short Data mining of protein sequences with amino acid position-based feature encoding technique
title_full Data mining of protein sequences with amino acid position-based feature encoding technique
title_fullStr Data mining of protein sequences with amino acid position-based feature encoding technique
title_full_unstemmed Data mining of protein sequences with amino acid position-based feature encoding technique
title_sort data mining of protein sequences with amino acid position-based feature encoding technique
publisher Springer Verlag
publishDate 2014
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958554492&doi=10.1007%2f978-981-4585-18-7_14&partnerID=40&md5=ae7e119e8dce09e407f1d3801dd87933
http://eprints.utp.edu.my/31712/
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score 11.62408

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