Abstract
MicroRNA (miRNA) is involved in many life processes and is closely associate with complex diseases such as cancer. Therefore, predicting the association between miRNA and disease has become a research hotspot. The bioinformatics method has great advantages, it is efficient, fast and less expensive. We have developed a reinforcement learning-Based model for Human microRNA-disease association prediction. This model puts three sub-method models CMF, NRLMF and LapRLS into the Q-learning model of reinforcement learning. We can get an optimal weight value. On the benchmark data set, the results of our method are comparable and even better than existing models.
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Acknowledgement
This paper is supported by the National Natural Science Foundation of China (62073231, 61772357, 61902272, 61876217, 61902271), National Research Project (2020YFC2006602) and Anhui Province Key Laboratory Research Project (IBBE2018KX09).
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Cui, L. et al. (2021). A Reinforcement Learning-Based Model for Human MicroRNA-Disease Association Prediction. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12838. Springer, Cham. https://doi.org/10.1007/978-3-030-84532-2_20
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DOI: https://doi.org/10.1007/978-3-030-84532-2_20
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