Efficient feature selection and classification of protein sequence data in bioinformatics
Bioinformatics has been an emerging area of research for the last three decades. The ultimate aims of bioinformatics were to store and manage the biological data, and develop and analyze computational tools to enhance their understanding. The size of data accumulated under various sequencing project...
| Main Authors: | Iqbal, M.J., Faye, I., Samir, B.B., Md Said, A. |
|---|---|
| Format: | Article |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.32341 / |
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Hindawi Publishing Corporation
2014
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904113027&doi=10.1155%2f2014%2f173869&partnerID=40&md5=f280cf37fafc0a3810f3bf162a4cf8ae http://eprints.utp.edu.my/32341/ |
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utp-eprints.323412022-03-29T05:27:34Z Efficient feature selection and classification of protein sequence data in bioinformatics Iqbal, M.J. Faye, I. Samir, B.B. Md Said, A. Bioinformatics has been an emerging area of research for the last three decades. The ultimate aims of bioinformatics were to store and manage the biological data, and develop and analyze computational tools to enhance their understanding. The size of data accumulated under various sequencing projects is increasing exponentially, which presents difficulties for the experimental methods. To reduce the gap between newly sequenced protein and proteins with known functions, many computational techniques involving classification and clustering algorithms were proposed in the past. The classification of protein sequences into existing superfamilies is helpful in predicting the structure and function of large amount of newly discovered proteins. The existing classification results are unsatisfactory due to a huge size of features obtained through various feature encoding methods. In this work, a statistical metric-based feature selection technique has been proposed in order to reduce the size of the extracted feature vector. The proposed method of protein classification shows significant improvement in terms of performance measure metrics: accuracy, sensitivity, specificity, recall, F-measure, and so forth. © 2014 Muhammad Javed Iqbal et al. Hindawi Publishing Corporation 2014 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904113027&doi=10.1155%2f2014%2f173869&partnerID=40&md5=f280cf37fafc0a3810f3bf162a4cf8ae Iqbal, M.J. and Faye, I. and Samir, B.B. and Md Said, A. (2014) Efficient feature selection and classification of protein sequence data in bioinformatics. Scientific World Journal, 2014 . http://eprints.utp.edu.my/32341/ |
| institution |
Universiti Teknologi Petronas |
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UTP Institutional Repository |
| description |
Bioinformatics has been an emerging area of research for the last three decades. The ultimate aims of bioinformatics were to store and manage the biological data, and develop and analyze computational tools to enhance their understanding. The size of data accumulated under various sequencing projects is increasing exponentially, which presents difficulties for the experimental methods. To reduce the gap between newly sequenced protein and proteins with known functions, many computational techniques involving classification and clustering algorithms were proposed in the past. The classification of protein sequences into existing superfamilies is helpful in predicting the structure and function of large amount of newly discovered proteins. The existing classification results are unsatisfactory due to a huge size of features obtained through various feature encoding methods. In this work, a statistical metric-based feature selection technique has been proposed in order to reduce the size of the extracted feature vector. The proposed method of protein classification shows significant improvement in terms of performance measure metrics: accuracy, sensitivity, specificity, recall, F-measure, and so forth. © 2014 Muhammad Javed Iqbal et al. |
| format |
Article |
| author |
Iqbal, M.J. Faye, I. Samir, B.B. Md Said, A. |
| spellingShingle |
Iqbal, M.J. Faye, I. Samir, B.B. Md Said, A. Efficient feature selection and classification of protein sequence data in bioinformatics |
| author_sort |
Iqbal, M.J. |
| title |
Efficient feature selection and classification of protein sequence data in bioinformatics |
| title_short |
Efficient feature selection and classification of protein sequence data in bioinformatics |
| title_full |
Efficient feature selection and classification of protein sequence data in bioinformatics |
| title_fullStr |
Efficient feature selection and classification of protein sequence data in bioinformatics |
| title_full_unstemmed |
Efficient feature selection and classification of protein sequence data in bioinformatics |
| title_sort |
efficient feature selection and classification of protein sequence data in bioinformatics |
| publisher |
Hindawi Publishing Corporation |
| publishDate |
2014 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904113027&doi=10.1155%2f2014%2f173869&partnerID=40&md5=f280cf37fafc0a3810f3bf162a4cf8ae http://eprints.utp.edu.my/32341/ |
| _version_ |
1741197720170790912 |
| score |
11.62408 |