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A graph regularized non-negative matrix factorization method for identifying microRNA-disease associations
Qiu Xiao1, Jiawei Luo1, Cheng Liang2
1College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.
Motivation:
MicroRNAs (miRNAs) play crucial roles in post-transcriptional regulations and various cellular processes. The identification of disease-related miRNAs provides great insights into the underlying pathogenesis of diseases at a system level. However, most existing computational approaches are biased towards known miRNA-disease associations, which is inappropriate for those new diseases or miRNAs without any known association information.
Results:
In this study, we propose a new method with graph regularized non-negative matrix factorization in heterogeneous omics data, called GRNMF, to discover potential associations between miRNAs and diseases, especially for new diseases and miRNAs or those diseases and miRNAs with sparse known associations. First, we integrate the disease semantic information and miRNA functional information to estimate disease similarity and miRNA similarity, respectively. Considering that there is no available interaction observed for new diseases or miRNAs, a preprocessing step is developed to construct the interaction score profiles that will assist in prediction. Next, a graph regularized non-negative matrix factorization framework is utilized to simultaneously identify potential associations for all diseases. The results indicated that our proposed method can effectively prioritize disease-associated miRNAs with higher accuracy compared with other recent approaches. Moreover, case studies also demonstrated the effectiveness of GRNMF to infer unknown miRNA-disease associations for those novel diseases and miRNAs.
Availability And Implementation:
The code of GRNMF is freely available at https://github.com/XIAO-HN/GRNMF/.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
Insights
This study introduces GRNMF, a novel computational method for identifying potential microRNA (miRNA) and disease associations. GRNMF effectively discovers new links, especially for novel diseases and miRNAs, outperforming existing approaches.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- MicroRNAs (miRNAs) are key regulators in cellular processes.
- Identifying disease-associated miRNAs offers insights into disease pathogenesis.
- Existing methods struggle with novel or sparsely associated miRNAs and diseases.
Purpose of the Study:
- To develop a computational method for discovering novel miRNA-disease associations.
- To address limitations of existing approaches biased towards known associations.
- To improve identification of associations for new or under-represented miRNAs and diseases.
Main Methods:
- Proposed Graph Regularized Non-negative Matrix Factorization (GRNMF) method.
- Integrated disease semantic and miRNA functional information for similarity estimation.
- Developed a preprocessing step to construct interaction score profiles for novel entities.
- Utilized a graph regularized non-negative matrix factorization framework for simultaneous prediction.
Main Results:
- GRNMF effectively prioritizes disease-associated miRNAs with high accuracy.
- The method outperforms recent computational approaches.
- Case studies validated GRNMF's effectiveness in inferring unknown miRNA-disease associations for novel diseases and miRNAs.
Conclusions:
- GRNMF offers a robust framework for discovering novel miRNA-disease associations.
- The method is particularly valuable for exploring associations involving new or under-characterized miRNAs and diseases.
- GRNMF enhances our understanding of miRNA roles in disease pathogenesis.
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