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A vector projection similarity-based method for miRNA-disease association prediction
Guobo Xie1, Weijie Xie1, Guosheng Gu1
1School of Computer, Guangdong University of Technology, Guangzhou, 510000, China.
Abstract:
[S U M M A R Y] Many miRNA-disease association prediction models incorporate Gaussian interaction profile kernel similarity (GIPS). However, the GIPS fails to consider the specificity of the miRNA-disease association matrix, where matrix elements with a value of 0 represent miRNA and disease relationships that have not been discovered yet. To address this issue and better account for the impact of known and unknown miRNA-disease associations on similarity, we propose a method called vector projection similarity-based method for miRNA-disease association prediction (VPSMDA). In VPSMDA, we introduce three projection rules and combined with logistic functions for the miRNA-disease association matrix and propose a vector projection similarity measure for miRNAs and diseases. By integrating the vector projection similarity matrix with the original one, we obtain the improved miRNA and disease similarity matrix. Additionally, we construct a weight matrix using different numbers of neighbors to reduce the noise in the similarity matrix. In performance evaluation, both LOOCV and 5-fold CV experiments demonstrate that VPSMDA outperforms seven other state-of-the-art methods in AUC. Furthermore, in a case study, VPSMDA successfully predicted 10, 9, and 10 out of the top 10 associations for three important human diseases, respectively, and these predictions were confirmed by recent biomedical resources.
Insights
This study introduces a novel Vector Projection Similarity-based Method for miRNA-disease Association Prediction (VPSMDA). VPSMDA improves upon existing methods by better accounting for known and unknown miRNA-disease associations, achieving superior prediction accuracy.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Gaussian interaction profile kernel similarity (GIPS) is commonly used in miRNA-disease association prediction.
- GIPS overlooks the specificity of the miRNA-disease association matrix, particularly zero values representing undiscovered relationships.
Purpose of the Study:
- To propose a novel method, VPSMDA, for miRNA-disease association prediction.
- To enhance similarity measures by incorporating both known and unknown miRNA-disease associations.
- To improve the accuracy of predicting potential miRNA-disease links.
Main Methods:
- Developed a Vector Projection Similarity-based Method for miRNA-disease Association Prediction (VPSMDA).
- Introduced three projection rules and logistic functions to the miRNA-disease association matrix.
- Integrated vector projection similarity with the original matrix to create an improved similarity matrix.
- Constructed a weighted matrix using neighbor information to reduce noise.
Main Results:
- VPSMDA demonstrated superior performance compared to seven state-of-the-art methods in LOOCV and 5-fold CV experiments, achieving higher AUC values.
- A case study showed VPSMDA successfully predicted top 10 associations for three human diseases, with 10, 9, and 10 predictions confirmed by recent biomedical resources.
Conclusions:
- VPSMDA offers a more effective approach to miRNA-disease association prediction by considering matrix specificity.
- The method shows significant potential for identifying novel miRNA-disease relationships.
- VPSMDA outperforms existing prediction models and provides reliable predictions validated by external data.
