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

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@article{IJASEIT2346,
   author = {Sharon K. Guramad S and Rohayanti Hassan and Razib M. Othman and Hishammuddin Asmuni and Shahreen Kasim},
   title = {Incorporating Multiple Biology based Knowledge to Amplify the Prophecy of Enzyme Sub-Functional Classes},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {7},
   number = {4},
   year = {2017},
   pages = {1479--1485},
   keywords = {Enzyme sub-functional classes; amino acid composition; dipeptide composition; hydrophobicity and hydrophilicity; support vector machine;},
   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.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2346},    doi = {10.18517/ijaseit.7.4.2346} }

EndNote

%A Guramad S, Sharon K.
%A Hassan, Rohayanti
%A Othman, Razib M.
%A Asmuni, Hishammuddin
%A Kasim, Shahreen
%D 2017
%T Incorporating Multiple Biology based Knowledge to Amplify the Prophecy of Enzyme Sub-Functional Classes
%B 2017
%9 Enzyme sub-functional classes; amino acid composition; dipeptide composition; hydrophobicity and hydrophilicity; support vector machine;
%! Incorporating Multiple Biology based Knowledge to Amplify the Prophecy of Enzyme Sub-Functional Classes
%K Enzyme sub-functional classes; amino acid composition; dipeptide composition; hydrophobicity and hydrophilicity; support vector machine;
%X 

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.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2346 %R doi:10.18517/ijaseit.7.4.2346 %J International Journal on Advanced Science, Engineering and Information Technology %V 7 %N 4 %@ 2088-5334

IEEE

Sharon K. Guramad S,Rohayanti Hassan,Razib M. Othman,Hishammuddin Asmuni and Shahreen Kasim,"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, pp. 1479-1485, 2017. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.7.4.2346.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Guramad S, Sharon K.
AU  - Hassan, Rohayanti
AU  - Othman, Razib M.
AU  - Asmuni, Hishammuddin
AU  - Kasim, Shahreen
PY  - 2017
TI  - Incorporating Multiple Biology based Knowledge to Amplify the Prophecy of Enzyme Sub-Functional Classes
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 7 (2017) No. 4
Y2  - 2017
SP  - 1479
EP  - 1485
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Enzyme sub-functional classes; amino acid composition; dipeptide composition; hydrophobicity and hydrophilicity; support vector machine;
N2  - 

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.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2346 DO - 10.18517/ijaseit.7.4.2346

RefWorks

RT Journal Article
ID 2346
A1 Guramad S, Sharon K.
A1 Hassan, Rohayanti
A1 Othman, Razib M.
A1 Asmuni, Hishammuddin
A1 Kasim, Shahreen
T1 Incorporating Multiple Biology based Knowledge to Amplify the Prophecy of Enzyme Sub-Functional Classes
JF International Journal on Advanced Science, Engineering and Information Technology
VO 7
IS 4
YR 2017
SP 1479
OP 1485
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Enzyme sub-functional classes; amino acid composition; dipeptide composition; hydrophobicity and hydrophilicity; support vector machine;
AB 

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.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2346 DO - 10.18517/ijaseit.7.4.2346