Subcellular localization prediction of apoptosis proteins based on evolutionary information and support vector machine

Publication date: Available online 24 May 2017
Source:Artificial Intelligence in Medicine
Author(s): Qilin Xiang, Bo Liao, Xianhong Li, Huimin Xu, Jing Chen, Zhuoxing Shi, Qi Dai, Yuhua Yao
ObjectivesIn this paper, a high-quality sequence encoding scheme is proposed for predicting subcellular location of apoptosis proteins.MethodsIn the proposed methodology, the novel evolutionary-conservative information is introduced to represent protein sequences. Meanwhile, based on the proportion of golden section in mathematics, position-specific scoring matrix (PSSM) is divided into several blocks. Then, these features are predicted by support vector machine (SVM) and the predictive capability of proposed method is implemented by jackknife testResultsThe results show that the golden section method is better than no segmentation method. The overall accuracy for ZD98 and CL317 is 98.98% and 91.11%, respectively, which indicates that our method can play a complimentary role to the existing methods in the relevant areas.ConclusionsThe proposed feature representation is powerful and the prediction accuracy will be improved greatly, which denotes our method provides the state-of-the-art performance for predicting subcellular location of apoptosis proteins.



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