Incorporating multiple biology based knowledge to amplify the prophecy of enzyme sub-functional classes
Based on current in silico methods, enzyme sub-functional classes is distinguished from sequence level information, local order or sequence length and order knowledge. To date, no work has been done to predict the enzyme subclasses efficiently corresponding to the ENZYME database. In order to precis...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Published: |
Insight Society
2017
|
| Subjects: |
| _version_ | 1796862492960358400 |
|---|---|
| author | Guramads, S. K. Hassan, R. Othman, R. M. Asmuni, H. Kasim, S. |
| author_facet | Guramads, S. K. Hassan, R. Othman, R. M. Asmuni, H. Kasim, S. |
| author_sort | Guramads, S. K. |
| collection | ePrints |
| description | Based on current in silico methods, enzyme sub-functional classes is distinguished from sequence level information, local order or sequence length and order knowledge. To date, no work has been done to predict the enzyme subclasses efficiently corresponding to the ENZYME database. In order to precisely predict the sub-functional classes of enzyme, we propose a derivative feature vector labelled as APH which unifies amino acid composition, dipeptide composition, hydrophobicity and hydrophilicity. Support Vector Machine is used for prediction and the performance is evaluated using accuracy obtained over 99% and Matthew's Correlation Coefficient (MCC) over 0.99 with the aid of biological validation from in vivo studies. |
| first_indexed | 2024-03-05T20:12:26Z |
| format | Article |
| id | utm.eprints-76365 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T20:12:26Z |
| publishDate | 2017 |
| publisher | Insight Society |
| record_format | dspace |
| spelling | utm.eprints-763652018-06-29T22:24:57Z http://eprints.utm.my/76365/ Incorporating multiple biology based knowledge to amplify the prophecy of enzyme sub-functional classes Guramads, S. K. Hassan, R. Othman, R. M. Asmuni, H. Kasim, S. QA75 Electronic computers. Computer science Based on current in silico methods, enzyme sub-functional classes is distinguished from sequence level information, local order or sequence length and order knowledge. To date, no work has been done to predict the enzyme subclasses efficiently corresponding to the ENZYME database. In order to precisely predict the sub-functional classes of enzyme, we propose a derivative feature vector labelled as APH which unifies amino acid composition, dipeptide composition, hydrophobicity and hydrophilicity. Support Vector Machine is used for prediction and the performance is evaluated using accuracy obtained over 99% and Matthew's Correlation Coefficient (MCC) over 0.99 with the aid of biological validation from in vivo studies. Insight Society 2017 Article PeerReviewed Guramads, S. K. and Hassan, R. and Othman, R. M. and Asmuni, H. and Kasim, S. (2017) Incorporating multiple biology based knowledge to amplify the prophecy of enzyme sub-functional classes. International Journal on Advanced Science, Engineering and Information Technology, 7 (4). pp. 1479-1485. ISSN 2088-5334 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029511702&doi=10.18517%2fijaseit.7.4.2346&partnerID=40&md5=5e9948c8a0d54a25d458c19297c1b4f8 |
| spellingShingle | QA75 Electronic computers. Computer science Guramads, S. K. Hassan, R. Othman, R. M. Asmuni, H. Kasim, S. Incorporating multiple biology based knowledge to amplify the prophecy of enzyme sub-functional classes |
| title | Incorporating multiple biology based knowledge to amplify the prophecy of enzyme sub-functional classes |
| title_full | Incorporating multiple biology based knowledge to amplify the prophecy of enzyme sub-functional classes |
| title_fullStr | Incorporating multiple biology based knowledge to amplify the prophecy of enzyme sub-functional classes |
| title_full_unstemmed | Incorporating multiple biology based knowledge to amplify the prophecy of enzyme sub-functional classes |
| title_short | Incorporating multiple biology based knowledge to amplify the prophecy of enzyme sub-functional classes |
| title_sort | incorporating multiple biology based knowledge to amplify the prophecy of enzyme sub functional classes |
| topic | QA75 Electronic computers. Computer science |
| work_keys_str_mv | AT guramadssk incorporatingmultiplebiologybasedknowledgetoamplifytheprophecyofenzymesubfunctionalclasses AT hassanr incorporatingmultiplebiologybasedknowledgetoamplifytheprophecyofenzymesubfunctionalclasses AT othmanrm incorporatingmultiplebiologybasedknowledgetoamplifytheprophecyofenzymesubfunctionalclasses AT asmunih incorporatingmultiplebiologybasedknowledgetoamplifytheprophecyofenzymesubfunctionalclasses AT kasims incorporatingmultiplebiologybasedknowledgetoamplifytheprophecyofenzymesubfunctionalclasses |