Hassan, U. Kalsum and A. Shah, Zuraini and M. Othman, Razib and Hassan, Rohayanti and M. Rahim, Shafry and Asmuni, Hishammudin and Talib, Jumail and Zakaria, Zalmiyah (2009) SPlitSSI-SVM: an algorithm to reduce the misleading and increase the strength of domain signal. Computers in Biology and Medicine, 39 (11). 1013 -1019. ISSN 0010-4825
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Official URL: http://dx.doi.org/10.1016/j.compbiomed.2009.08.002
Protein domains contain information about the prediction of protein structure, function, evolution and design since the protein sequence may contain several domains with different or the same copies of the protein domain. In this study, we proposed an algorithm named SplitSSI-SVM that works with the following steps. First, the training and testing datasets are generated to test the SplitSSI-SVM. Second, the protein sequence is split into subsequence based on order and disorder regions. The protein sequence that is more than 600 residues is split into subsequences to investigate the effectiveness of the protein domain prediction based on subsequence. Third, multiple sequence alignment is performed to predict the secondary structure using bidirectional recurrent neural networks (BRNN) where BRNN considers the interaction between amino acids. The information of about protein secondary structure is used to increase the protein domain boundaries signal. Lastly, support vector machines (SVM) are used to classify the protein domain into single-domain, two-domain and multiple-domain. The SplitSSI-SVM is developed to reduce misleading signal, lower protein domain signal caused by primary structure of protein sequence and to provide accurate classification of the protein domain. The performance of SplitSSI-SVM is evaluated using sensitivity and specificity on single-domain, two-domain and multiple-domain. The evaluation shows that the SplitSSI-SVM achieved better results compared with other protein domain predictors such as DOMpro, GlobPlot, Dompred-DPS, Mateo, Biozon, Armadillo, KemaDom, SBASE, HMMPfam and HMMSMART especially in two-domain and multiple-domain.
|Uncontrolled Keywords:||bidirectional recurrent neural networks, protein domain prediction, protein secondary structure information, protein subsequence, support vector machines|
|Subjects:||T Technology > TK Electrical engineering. Electronics Nuclear engineering|
|Divisions:||Computer Science and Information System|
|Deposited By:||Liza Porijo|
|Deposited On:||18 Jul 2011 08:04|
|Last Modified:||18 Jul 2011 08:04|
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