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Published on: May 1, 2021
Predicting miRNA-disease association based on inductive matrix completion
Xing Chen1, Lei Wang1, Jia Qu1
1School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.
Motivation:
It has been shown that microRNAs (miRNAs) play key roles in variety of biological processes associated with human diseases. In Consideration of the cost and complexity of biological experiments, computational methods for predicting potential associations between miRNAs and diseases would be an effective complement.
Results:
This paper presents a novel model of Inductive Matrix Completion for MiRNA-Disease Association prediction (IMCMDA). The integrated miRNA similarity and disease similarity are calculated based on miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity. The main idea is to complete the missing miRNA-disease association based on the known associations and the integrated miRNA similarity and disease similarity. IMCMDA achieves AUC of 0.8034 based on leave-one-out-cross-validation and improved previous models. In addition, IMCMDA was applied to five common human diseases in three types of case studies. In the first type, respectively, 42, 44, 45 out of top 50 predicted miRNAs of Colon Neoplasms, Kidney Neoplasms, Lymphoma were confirmed by experimental reports. In the second type of case study for new diseases without any known miRNAs, we chose Breast Neoplasms as the test example by hiding the association information between the miRNAs and Breast Neoplasms. As a result, 50 out of top 50 predicted Breast Neoplasms-related miRNAs are verified. In the third type of case study, IMCMDA was tested on HMDD V1.0 to assess the robustness of IMCMDA, 49 out of top 50 predicted Esophageal Neoplasms-related miRNAs are verified.
Availability And Implementation:
The code and dataset of IMCMDA are freely available at https://github.com/IMCMDAsourcecode/IMCMDA.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
Insights
Computational methods can predict microRNA-disease associations, complementing experiments. A novel Inductive Matrix Completion for MiRNA-Disease Association (IMCMDA) model achieved high accuracy and validated predictions for multiple human diseases.
Area of Science:
- Genomics and Bioinformatics
- Computational Biology
- Molecular Medicine
Background:
- MicroRNAs (miRNAs) are crucial regulators in biological processes and human diseases.
- Experimental identification of miRNA-disease associations is costly and complex.
- Computational approaches offer an efficient complement for predicting these associations.
Purpose of the Study:
- To develop and validate a novel computational model, Inductive Matrix Completion for MiRNA-Disease Association (IMCMDA), for predicting miRNA-disease associations.
- To integrate multiple similarity measures for enhanced prediction accuracy.
- To assess the model's performance and robustness across various human diseases.
Main Methods:
- Developed the IMCMDA model utilizing Inductive Matrix Completion.
- Integrated miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity.
- Calculated integrated miRNA and disease similarities to infer missing associations.
Main Results:
- Achieved an Area Under the Curve (AUC) of 0.8034 via leave-one-out cross-validation, outperforming previous models.
- Successfully predicted known miRNA-disease associations for Colon Neoplasms, Kidney Neoplasms, and Lymphoma (42-45/50 confirmed).
- Demonstrated high accuracy in predicting novel associations for Breast Neoplasms (50/50 confirmed) and Esophageal Neoplasms (49/50 confirmed).
Conclusions:
- The IMCMDA model provides an effective computational tool for predicting miRNA-disease associations.
- The model demonstrates high accuracy and robustness, validated across multiple disease datasets.
- IMCMDA facilitates the discovery of novel miRNA-disease links, aiding biomedical research.
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