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| Content Provider | PubMed Central |
|---|---|
| Author | Iqbal, Muhammad Javed Faye, Ibrahima Samir, Brahim Belhaouari Said, Abas Md. |
| Copyright Year | 2014 |
| Abstract | 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. |
| Related Links | http://dx.doi.org/10.1155/2014/173869 |
| Starting Page | 173869 |
| File Format | |
| ISSN | 1537744X |
| e-ISSN | 1537744X |
| Journal | The Scientific World Journal |
| Volume Number | 2014 |
| Language | English |
| Publisher | Hindawi Publishing Corporation |
| Publisher Date | 2014-01-01 |
| Access Restriction | Open |
| Rights Holder | Hindawi Publishing Corporation |
| Subject Keyword | Research in Higher Education |
| Content Type | Text |
| Resource Type | Article |
| Subject | Medicine Biochemistry, Genetics and Molecular Biology Environmental Science |
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