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MVNMDA: A Multi-View Network Combing Semantic and Global Features for Predicting miRNA-Disease Association.
Chen Yang1, Zhen Wang1, Shanwen Zhang1
1School of Electronic Infomation, Xijing University, Xi'an 710123, China.
Molecules (Basel, Switzerland)
|January 11, 2024
Summary
This study introduces MVNMDA, a novel computational model for predicting microRNA-disease associations. MVNMDA integrates multi-view features to improve accuracy, offering a more efficient approach for disease screening and treatment development.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- MicroRNAs (miRNAs) are crucial in human disease development and progression.
- Identifying miRNA-disease associations is vital for disease screening and treatment.
- Traditional experimental methods for miRNA-disease association identification are laborious, time-consuming, and struggle with large datasets.
Purpose of the Study:
- To develop an efficient computational model for predicting potential miRNA-disease associations.
- To overcome limitations of existing methods that rely on incomplete external databases.
- To improve the accuracy and applicability of miRNA-disease association prediction.
Main Methods:
- A multi-view computational model, MVNMDA, was proposed, integrating local, global, and semantic views of miRNA and disease features.
- Known association information was used to construct initial node features.
- Multiple networks were built to extract low-dimensional feature embeddings.
- A cascaded attention classifier was employed for feature fusion and precise prediction.
Main Results:
- MVNMDA demonstrated superior performance compared to existing computational methods on the HMDD v2.0 and HMDD v3.2 datasets.
- Extensive experiments validated the effectiveness of the proposed multi-view approach.
- Case studies confirmed the reliable predictive performance of MVNMDA.
Conclusions:
- MVNMDA offers an effective and efficient computational approach for predicting miRNA-disease associations.
- The model's multi-view integration strategy enhances prediction accuracy.
- MVNMDA has significant potential for advancing disease screening and therapeutic strategies.
