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Three-Layer Heterogeneous Network Combined With Unbalanced Random Walk for miRNA-Disease Association Prediction
Limin Yu1,2, Xianjun Shen1,2, Duo Zhong1,2
1School of Computer, Central China Normal University, Wuhan, China.
Abstract:
miRNA plays an important role in many biological processes, and increasing evidence shows that miRNAs are closely related to human diseases. Most existing miRNA-disease association prediction methods were only based on data related to miRNAs and diseases and failed to effectively use other existing biological data. However, experimentally verified miRNA-disease associations are limited, there are complex correlations between biological data. Therefore, we propose a novel Three-layer heterogeneous network Combined with unbalanced Random Walk for MiRNA-Disease Association prediction algorithm (TCRWMDA), which can effectively integrate multi-source association data. TCRWMDA based not only on the known miRNA-disease associations, also add the new priori information (lncRNA-miRNA and lncRNA-disease associations) to build a three-layer heterogeneous network, lncRNA was added as the transition path of the intermediate point to mine more effective information between networks. The AUC value obtained by the TCRWMDA algorithm on 5-fold cross validation is 0.9209, compared with other models based on the same similarity calculation method, TCRWMDA obtained better results. TCRWMDA was applied to the analysis of four types of cancer, the results proved that TCRWMDA is an effective tool to predict the potential miRNA-disease association. The source code and dataset of TCRWMDA are available at: https://github.com/ylm0505/TCRWMDA.
Insights
This study introduces TCRWMDA, a new method for predicting miRNA-disease associations by integrating multiple biological data sources. The approach effectively identifies potential links between microRNAs (miRNAs) and diseases, aiding in disease research.
Area of Science:
- Biomedical Informatics
- Genomics
- Computational Biology
Background:
- MicroRNAs (miRNAs) are crucial in biological processes and implicated in human diseases.
- Current prediction methods for miRNA-disease associations often overlook diverse biological data, limiting their effectiveness.
- The scarcity of experimentally verified miRNA-disease associations necessitates advanced predictive tools.
Purpose of the Study:
- To develop a novel algorithm for predicting miRNA-disease associations by integrating multi-source biological data.
- To enhance the accuracy of miRNA-disease association prediction beyond existing methods.
- To leverage lncRNA data as an intermediate layer for richer network analysis.
Main Methods:
- Proposed a Three-layer heterogeneous network Combined with unbalanced Random Walk for MiRNA-Disease Association prediction algorithm (TCRWMDA).
- Constructed a three-layer heterogeneous network incorporating known miRNA-disease, lncRNA-miRNA, and lncRNA-disease associations.
- Utilized lncRNAs as a transitional pathway to mine deeper inter-network information.
Main Results:
- Achieved an Area Under the Curve (AUC) value of 0.9209 on 5-fold cross-validation.
- Demonstrated superior performance compared to other models using the same similarity calculation methods.
- Successfully applied TCRWMDA to predict potential miRNA-disease associations in four types of cancer.
Conclusions:
- TCRWMDA effectively integrates multi-source association data for robust miRNA-disease prediction.
- The algorithm proves to be a valuable tool for identifying potential miRNA-disease associations, particularly in cancer research.
- The developed method offers a significant advancement in the field of computational disease association prediction.
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