Abstract

Numerous studies have confirmed that microRNAs play a crucial role in the research of complex human diseases. Identifying the relationship between miRNAs and diseases is important for improving the treatment of complex diseases. However, traditional biological experiments are not without restrictions. It is an urgent necessity for computational simulation to predict unknown miRNA-disease associations. In this work, we combine Q-learning algorithm of reinforcement learning to propose a RFLMDA model, three submodels CMF, NRLMF, and LapRLS are fused via Q-learning algorithm to obtain the optimal weight . The performance of RFLMDA was evaluated through five-fold cross-validation and local validation. As a result, the optimal weight is obtained as S (0.1735, 0.2913, 0.5352), and the AUC is 0.9416. By comparing the experiments with other methods, it is proved that RFLMDA model has better performance. For better validate the predictive performance of RFLMDA, we use eight diseases for local verification and carry out case study on three common human diseases. Consequently, all the top 50 miRNAs related to Colorectal Neoplasms and Breast Neoplasms have been confirmed. Among the top 50 miRNAs related to Colon Neoplasms, Gastric Neoplasms, Pancreatic Neoplasms, Kidney Neoplasms, Esophageal Neoplasms, and Lymphoma, we confirm 47, 41, 49, 46, 46 and 48 miRNAs respectively.

Highlights

  • MicroRNA is a type of single-stranded endogenous non-coding RNA

  • In order to further validate the predictive performance of RFLMDA, we use eight diseases for local verification and perform case study on three common human diseases

  • The first sub-model used in the experiments is Collaborative matrix factorization (CMF) model [24] which is a classic baseline, it is often used for comparison in recommendation system related studies such as rating prediction and cold-start recommendations

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Summary

A Novel Reinforcement

Suzhou Key Laboratory of Mobile Network Technology and Application, Suzhou University of Science and Technology, Suzhou 215009, China. Chongqing Industrial Big Data Innovation Center Co., Ltd., Chongqing 400707, China

Introduction
Human miRNA-Disease Associations
MiRNA Functional Similarity
Disease Semantic Similarity
Collaborative Matrix Factorization
Neighborhood Regularized Logistic Matrix Factorization
Laplacian Regularized Least Squares
Reinforcement Learning
RFLMDA
3.1.Evaluation
AUPR and AUC of other
Comparison with Other Methods
Case Study
Breast Neoplasms
Lymphoma
Conclusions and Discussion
Full Text
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