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Int. J. Mol. Sci. 2019, 20(2), 302;

KSIMC: Predicting Kinase–Substrate Interactions Based on Matrix Completion

School of Computer Science and Engineering, Yulin Normal University, Yulin 537000, China
School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China
State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi University, Nanning 530004, China
Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning 530021, China
Authors to whom correspondence should be addressed.
Received: 4 December 2018 / Revised: 31 December 2018 / Accepted: 7 January 2019 / Published: 14 January 2019
(This article belongs to the Special Issue Protein Phosphorylation in Health and Disease)
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Protein phosphorylation is an important chemical modification catalyzed by kinases. It plays important roles in many cellular processes. Predicting kinase–substrate interactions is vital to understanding the mechanism of many diseases. Many computational methods have been proposed to identify kinase–substrate interactions. However, the prediction accuracy still needs to be improved. Therefore, it is necessary to develop an efficient computational method to predict kinase–substrate interactions. In this paper, we propose a novel computational approach, KSIMC, to identify kinase–substrate interactions based on matrix completion. Firstly, the kinase similarity and substrate similarity are calculated by aligning sequence of kinase–kinase and substrate–substrate, respectively. Then, the original association network is adjusted based on the similarities. Finally, the matrix completion is used to predict potential kinase–substrate interactions. The experiment results show that our method outperforms other state-of-the-art algorithms in performance. Furthermore, the relevant databases and scientific literature verify the effectiveness of our algorithm for new kinase–substrate interaction identification. View Full-Text
Keywords: protein phosphorylation; kinase-substrate interaction; heterogeneous network; matrix completion protein phosphorylation; kinase-substrate interaction; heterogeneous network; matrix completion

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Gan, J.; Qiu, J.; Deng, C.; Lan, W.; Chen, Q.; Hu, Y. KSIMC: Predicting Kinase–Substrate Interactions Based on Matrix Completion. Int. J. Mol. Sci. 2019, 20, 302.

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