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MiRNA-disease association prediction via hypergraph learning based on high-dimensionality features
Yu-Tian Wang1, Qing-Wen Wu1, Zhen Gao1
1School of Software, Qufu Normal University, Qufu, China.
Background:
MicroRNAs (miRNAs) have been confirmed to have close relationship with various human complex diseases. The identification of disease-related miRNAs provides great insights into the underlying pathogenesis of diseases. However, it is still a big challenge to identify which miRNAs are related to diseases. As experimental methods are in general expensive and time-consuming, it is important to develop efficient computational models to discover potential miRNA-disease associations.
Methods:
This study presents a novel prediction method called HFHLMDA, which is based on high-dimensionality features and hypergraph learning, to reveal the association between diseases and miRNAs. Firstly, the miRNA functional similarity and the disease semantic similarity are integrated to form an informative high-dimensionality feature vector. Then, a hypergraph is constructed by the K-Nearest-Neighbor (KNN) method, in which each miRNA-disease pair and its k most relevant neighbors are linked as one hyperedge to represent the complex relationships among miRNA-disease pairs. Finally, the hypergraph learning model is designed to learn the projection matrix which is used to calculate uncertain miRNA-disease association score.
Result:
Compared with four state-of-the-art computational models, HFHLMDA achieved best results of 92.09% and 91.87% in leave-one-out cross validation and fivefold cross validation, respectively. Moreover, in case studies on Esophageal neoplasms, Hepatocellular Carcinoma, Breast Neoplasms, 90%, 98%, and 96% of the top 50 predictions have been manually confirmed by previous experimental studies.
Conclusion:
MiRNAs have complex connections with many human diseases. In this study, we proposed a novel computational model to predict the underlying miRNA-disease associations. All results show that the proposed method is effective for miRNA-disease association predication.
Insights
This study introduces HFHLMDA, a computational model for predicting microRNA-disease associations. The method effectively identifies potential links between microRNAs and diseases, aiding in understanding complex disease pathogenesis.
Area of Science:
- Biomedical Informatics
- Computational Biology
- Genomics
Background:
- MicroRNAs (miRNAs) play a crucial role in various human complex diseases.
- Identifying disease-related miRNAs is vital for understanding disease pathogenesis.
- Experimental methods for miRNA-disease association identification are costly and time-consuming.
Purpose of the Study:
- To develop an efficient computational model for predicting potential miRNA-disease associations.
- To leverage high-dimensionality features and hypergraph learning for improved prediction accuracy.
Main Methods:
- The HFHLMDA method integrates miRNA functional similarity and disease semantic similarity.
- A hypergraph is constructed using the K-Nearest-Neighbor (KNN) method to represent complex miRNA-disease relationships.
- A hypergraph learning model is employed to calculate miRNA-disease association scores.
Main Results:
- HFHLMDA achieved superior performance compared to four state-of-the-art models.
- Validation demonstrated high accuracy with 92.09% (leave-one-out) and 91.87% (fivefold cross-validation).
- Case studies showed high confirmation rates for predicted associations (e.g., 90-98% for top 50 predictions).
Conclusions:
- The proposed HFHLMDA method is effective for predicting miRNA-disease associations.
- This computational approach offers a valuable tool for accelerating the discovery of disease-related miRNAs.
