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MSFSP: A Novel miRNA-Disease Association Prediction Model by Federating Multiple-Similarities Fusion and Space
Yi Zhang1, Min Chen2, Xiaohui Cheng1
1School of Information Science and Engineering, Guilin University of Technology, Guilin, China.
Abstract:
Growing evidences have indicated that microRNAs (miRNAs) play a significant role relating to many important bioprocesses; their mutations and disorders will cause the occurrence of various complex diseases. The prediction of miRNAs associated with underlying diseases via computational approaches is beneficial to identify biomarkers and discover specific medicine, which can greatly reduce the cost of diagnosis, cure, prognosis, and prevention of human diseases. However, how to further achieve a more reliable prediction of potential miRNA-disease associations with effective integration of different biological data is a challenge for researchers. In this study, we proposed a computational model by using a federated method of combined multiple-similarities fusion and space projection (MSFSP). MSFSP firstly fused the integrated disease similarity (composed of disease semantic similarity, disease functional similarity, and disease Hamming similarity) with the integrated miRNA similarity (composed of miRNA functional similarity, miRNA sequence similarity, and miRNA Hamming similarity). Secondly, it constructed the weighted network of miRNA-disease associations from the experimentally verified Boolean network of miRNA-disease associations by using similarity networks. Finally, it calculated the prediction results by weighting miRNA space projection scores and the disease space projection scores. Leave-one-out cross-validation demonstrated that MSFSP has the distinguished predictive accuracy with area under the receiver operating characteristics curve (AUC) of 0.9613 better than that of five other existing models. In case studies, the predictive ability of MSFSP was further confirmed as 96 and 98% of the top 50 predictions for prostatic neoplasms and lung neoplasms were successfully validated by experimental evidences and supporting experimental evidences were also found for 100% of the top 50 predictions for isolated diseases.
Insights
This study introduces a computational model for predicting microRNA-disease associations. The novel method enhances accuracy in identifying disease biomarkers and potential drug targets.
Area of Science:
- Biomedical Informatics
- Computational Biology
- Genomics
Background:
- MicroRNAs (miRNAs) are crucial in biological processes; their dysregulation is linked to complex diseases.
- Predicting miRNA-disease associations computationally aids biomarker discovery and therapeutic development.
- Integrating diverse biological data for reliable miRNA-disease association prediction remains a challenge.
Purpose of the Study:
- To propose an effective computational model, MSFSP, for predicting miRNA-disease associations.
- To integrate multiple similarity measures for both miRNAs and diseases.
- To enhance the accuracy of identifying potential miRNA-disease links.
Main Methods:
- Developed a federated method combining multiple-similarities fusion and space projection (MSFSP).
- Fused integrated disease similarities (semantic, functional, Hamming) with integrated miRNA similarities (functional, sequence, Hamming).
- Constructed a weighted miRNA-disease association network and employed space projection for prediction.
Main Results:
- MSFSP achieved a high predictive accuracy with an AUC of 0.9613, outperforming five existing models.
- Case studies showed 96% and 98% validation for top predictions in prostatic and lung neoplasms.
- 100% of top predictions for isolated diseases were supported by experimental evidence.
Conclusions:
- The MSFSP model offers a reliable and accurate approach for predicting miRNA-disease associations.
- This method can significantly reduce costs associated with disease diagnosis and treatment.
- MSFSP facilitates the identification of novel biomarkers and therapeutic targets for various diseases.
