Abstract
Accurate identification of drug-target interactions (DTIs) is essential for drug development. It not only helps the researchers to understand the mechanism of drug action, but also contributes to the innovative drug discovery and repositioning. However, due to the limitation the high cost and long time, the traditional experimental methods are difficult to be widely applied for DTIs prediction. In this study, we propose an in silico method for predicting drug-target interactions by Node2vec node embedding in molecular associations network (MAN). Specifically, the MAN is constructed by integrating the associations among drug, protein, disease, lncRNA and miRNA. Then, the node2vec embedding method is employed to obtain a behavior feature vector of each node in the network. The traditional attribute feature vector comes from the drug molecular fingerprint and protein sequences. Finally, a random forest (RF) classifier is performed on these features to predict potential drug-target pairs. The experimental results show that the behavior feature could obtain 87.37% accuracy, which is obviously better than the traditional attribute feature. This work is not only more robust and reliable for predicting DTIs, but also provides an alternative way for other biomolecules associations prediction.
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Acknowledgments
This work is supported by the Xinjiang Natural Science Foundation under Grant 2017D01A78. This work is also supported in part by the National Natural Science Foundation of China, under Grants 61902342.
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ZH Chen and ZH You conceived the algorithm, carried out analyses, prepared the data sets, carried out experiments, and wrote the manuscript; ZH Guo and HC Yi designed, performed; GX Luo and YB W analyzed experiments and checked the manuscript; All authors read and approved the final manuscript.
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Chen, ZH., You, ZH., Guo, ZH., Yi, HC., Luo, GX., Wang, YB. (2020). Predicting Drug-Target Interactions by Node2vec Node Embedding in Molecular Associations Network. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12464. Springer, Cham. https://doi.org/10.1007/978-3-030-60802-6_31
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DOI: https://doi.org/10.1007/978-3-030-60802-6_31
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