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Incorporating multiple biology based knowledge to amplify the prophecy of enzyme sub-functional classes

Guramad S., Sharon K. and Hassan, Rohayanti and M. Othman, Razib and Asmuni, Hishammuddin and Kasim, Shahreen (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 20885334

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: Enzyme sub-functional classes; amino acid composition; dipeptide composition; hydrophobicity and hydrophilicity; support vector machine
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Computer Science and Information Technology > Department of Web Technology
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 31 Mar 2019 07:37
Last Modified: 31 Mar 2019 07:37
URI: http://eprints.uthm.edu.my/id/eprint/10921
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