Abstract
Predicting the relationships between drugs and targets is a crucial step in the course of drug discovery and development. Computational prediction of associations between drugs and targets greatly enhances the probability of finding new interactions by reducing the cost of in vitro experiments. In this paper, a Meta-path-based Representation Learning model, namely MRLDTI, is proposed to predict unknown DTIs. Specifically, we first design a random walk strategy with a meta-path to collect the biological relations of drugs and targets. Then, the representations of drugs and targets are captured by a heterogeneous skip-gram algorithm. Finally, a machine learning classifier is employed by MRLDTI to discover novel DTIs. Experimental results indicate that MRLDTI performs better than several state-of-the-art models under ten-fold cross-validation on the gold standard dataset.
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Acknowledgments
This work was supported in part by the Natural Science Foundation of Xinjiang Uygur Autonomous Region under grant 2021D01D05, in part by the Pioneer Hundred Talents Program of Chinese Academy of Sciences, in part by the National Natural Science Foundation of China, under Grants 61702444, 62002297, 61902342, in part by Awardee of the NSFC Excellent Young Scholars Program, under Grant 61722212, and in part by the Tianshan youth - Excellent Youth, under Grant 2019Q029.
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Zhao, BW. et al. (2022). MRLDTI: A Meta-path-Based Representation Learning Model for Drug-Target Interaction Prediction. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13394. Springer, Cham. https://doi.org/10.1007/978-3-031-13829-4_39
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