DOI : https://doi.org/10.18517/ijaseit.7.4.2346

Incorporating Multiple Biology based Knowledge to Amplify the Prophecy of Enzyme Sub-Functional Classes

Sharon K. Guramad S (1), Rohayanti Hassan (2), Razib M. Othman (3), Hishammuddin Asmuni (4), Shahreen Kasim (5)
(1) Universiti Teknologi Malaysia, 81310 UTM Skudai, Malaysia
(2) Universiti Teknologi Malaysia, 81310 UTM Skudai, Malaysia
(3) Universiti Teknologi Malaysia, 81310 UTM Skudai, Malaysia
(4) Universiti Teknologi Malaysia, 81310 UTM Skudai, Malaysia
(5) Universiti Tun Hussein Onn Malaysia
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How to cite (IJASEIT) :
Guramad S, Sharon K., et al. “Incorporating Multiple Biology Based Knowledge to Amplify the Prophecy of Enzyme Sub-Functional Classes”. International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 4, Aug. 2017, pp. 1479-85, doi:10.18517/ijaseit.7.4.2346.
Citation Format :
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.
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