Abstract
Predicting drug-target interactions is very important to shorten the drug development cycle and reduce the cost of drug development. In this paper, we use a prediction framework based on knowledge graphs and binary classification models. Firstly, a knowledge graph is constructed using a drug database. Then, the entities in the knowledge graph are transformed into embedded vectors. Based on a dataset of drug-target interactions, the embedded vectors corresponding to drugs and targets are used as input data, and whether there is an interaction between the drug and the target is used as the label input to a binary classification neural network model for training. The experimental results show that the accuracy of drug-target prediction can be improved, when the improved transR strategy is used to construct the embedding vectors and the BiLSTM binary classification neural network model with attention mechanism.
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Acknowledgements
The authors thank the members of Machine Learning and Artificial Intelligence Laboratory, School of Computer Science and Technology, Wuhan University of Science and Technology, for their helpful discussion within seminars. This work was supported by the Innovation and Entrepreneurship Training Program for University Students (2022169).
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Zhang, Y., Cheng, M. (2023). Drug-Target Interaction Prediction Based on Knowledge Graph Embedding and BiLSTM Networks. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14088. Springer, Singapore. https://doi.org/10.1007/978-981-99-4749-2_68
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DOI: https://doi.org/10.1007/978-981-99-4749-2_68
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