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...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/173869 |
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| author | Muhammad Javed Iqbal Ibrahima Faye Brahim Belhaouari Samir Abas Md Said |
| author_facet | Muhammad Javed Iqbal Ibrahima Faye Brahim Belhaouari Samir Abas Md Said |
| author_sort | Muhammad Javed Iqbal |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-c4d9950a444c453abcb7fe47660aa7ec |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-c4d9950a444c453abcb7fe47660aa7ec2025-02-03T01:31:03ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/173869173869Efficient Feature Selection and Classification of Protein Sequence Data in BioinformaticsMuhammad Javed Iqbal0Ibrahima Faye1Brahim Belhaouari Samir2Abas Md Said3Computer and Information Sciences Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 31750 Tronoh, Perak, MalaysiaFundamental and Applied Sciences Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 31750 Tronoh, Perak, MalaysiaCollege of Sciences, Alfaisal University, P.O. Box 50927, Riyadh 11533, Saudi ArabiaComputer and Information Sciences Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 31750 Tronoh, Perak, MalaysiaBioinformatics 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.http://dx.doi.org/10.1155/2014/173869 |
| spellingShingle | Muhammad Javed Iqbal Ibrahima Faye Brahim Belhaouari Samir Abas Md Said Efficient Feature Selection and Classification of Protein Sequence Data in Bioinformatics The Scientific World Journal |
| title | 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_short | Efficient Feature Selection and Classification of Protein Sequence Data in Bioinformatics |
| title_sort | efficient feature selection and classification of protein sequence data in bioinformatics |
| url | http://dx.doi.org/10.1155/2014/173869 |
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