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Published on: February 23, 2024
A heterogeneous label propagation approach to explore the potential associations between miRNA and disease
Xing Chen1, De-Hong Zhang2, Zhu-Hong You3
1School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China. xingchen@amss.ac.cn.
Background:
Research on microRNAs (miRNAs) has attracted increasingly worldwide attention over recent years as growing experimental results have made clear that miRNA correlates with masses of critical biological processes and the occurrence, development, and diagnosis of human complex diseases. Nonetheless, the known miRNA-disease associations are still insufficient considering plenty of human miRNAs discovered now. Therefore, there is an urgent need for effective computational model predicting novel miRNA-disease association prediction to save time and money for follow-up biological experiments.
Methods:
In this study, considering the insufficiency of the previous computational methods, we proposed the model named heterogeneous label propagation for MiRNA-disease association prediction (HLPMDA), in which a heterogeneous label was propagated on the multi-network of miRNA, disease and long non-coding RNA (lncRNA) to infer the possible miRNA-disease association. The strength of the data about lncRNA-miRNA association and lncRNA-disease association enabled HLPMDA to produce a better prediction.
Results:
HLPMDA achieved AUCs of 0.9232, 0.8437 and 0.9218 ± 0.0004 based on global and local leave-one-out cross validation and 5-fold cross validation, respectively. Furthermore, three kinds of case studies were implemented and 47 (esophageal neoplasms), 49 (breast neoplasms) and 46 (lymphoma) of top 50 candidate miRNAs were proved by experiment reports.
Conclusions:
All the results adequately showed that HLPMDA is a recommendable miRNA-disease association prediction method. We anticipated that HLPMDA could help the follow-up investigations by biomedical researchers.
Insights
A new computational model, HLPMDA, effectively predicts microRNA-disease associations. It leverages heterogeneous label propagation on miRNA, disease, and lncRNA networks, aiding biological research.
Area of Science:
- Bioinformatics
- Genomics
- Computational Biology
Background:
- MicroRNAs (miRNAs) are crucial in biological processes and human diseases.
- Existing miRNA-disease associations are insufficient for the vast number of known miRNAs.
- Effective computational models are needed to predict novel miRNA-disease associations.
Purpose of the Study:
- To develop an effective computational model for predicting novel miRNA-disease associations.
- To address the limitations of previous computational methods in miRNA-disease association prediction.
Main Methods:
- Proposed Heterogeneous Label Propagation for MiRNA-disease association prediction (HLPMDA).
- Utilized a multi-network incorporating miRNA, disease, and long non-coding RNA (lncRNA) data.
- Propagated heterogeneous labels across the integrated network to infer associations.
Main Results:
- HLPMDA achieved high prediction accuracy with AUCs of 0.9232 (global LOOCV), 0.8437 (local LOOCV), and 0.9218 (5-fold CV).
- Case studies validated predictions: 47/50 for esophageal neoplasms, 49/50 for breast neoplasms, and 46/50 for lymphoma.
- Experimental reports confirmed a significant portion of the top predicted miRNA-disease associations.
Conclusions:
- HLPMDA demonstrates strong performance as a miRNA-disease association prediction method.
- The model's accuracy and validated predictions support its utility for researchers.
- HLPMDA is expected to facilitate future biomedical investigations into miRNA-disease links.
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