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Published on: November 30, 2016
Prediction and validation of association between microRNAs and diseases by multipath methods
Xiangxiang Zeng1, Xuan Zhang1, Yuanlu Liao1
1Department of Computer Science, Xiamen University, Xiamen 361005, China.
Background:
Deciphering the genetic basis of human diseases is an important goal in biomedical research. There is increasing evidence suggesting that microRNAs play critical roles in many key biological processes. So the identification of microRNAs associated with disease is very important for understanding the pathogenesis of diseases.
Methods:
Two multipath methods are introduced to predict the associations between microRNAs and diseases based on microRNA-disease heterogeneous network. The first method, HeteSim_MultiPath (HSMP), uses the HeteSim measure to calculate the similarity between objects and combines the HeteSim scores of different paths with a constant that dampens the contributions of longer paths. The second one, HeteSim_SVM (HSSVM), uses the HeteSim measure and the machine learning method used to combine HeteSim scores instead of a constant.
Results:
We use the leave-one-out cross-validation to evaluate our novel methods, and find that our methods are better than other methods. We achieve an area under the ROC curve of 0.981 and 0.984 respectively. We also check the top-10 most similarity of microRNAs-diseases associations and find that our predictions are reasonable and credible.
Conclusions:
The encouraging results suggest that multipath methods can provide help in identifying novel microRNA-disease associations, and guide biological experiments for scientific research. This article is part of a Special Issue entitled "System Genetics". Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.
Insights
Novel multipath methods accurately predict microRNA-disease associations, aiding in understanding disease pathogenesis and guiding experimental research for biomedical discoveries.
Area of Science:
- Biomedical research
- Genetics
- Computational biology
Background:
- MicroRNAs (miRNAs) are crucial regulators of biological processes.
- Identifying miRNA-disease associations is vital for understanding disease pathogenesis.
- Genetic basis of human diseases is a key research area.
Purpose of the Study:
- To develop and evaluate novel computational methods for predicting microRNA-disease associations.
- To leverage heterogeneous networks and similarity measures for improved prediction accuracy.
Main Methods:
- Introduction of two multipath methods: HeteSim_MultiPath (HSMP) and HeteSim_SVM (HSSVM).
- Utilizing the HeteSim measure to calculate object similarity within a microRNA-disease heterogeneous network.
- Employing machine learning (HSSVM) and path-based constant dampening (HSMP) to combine similarity scores.
Main Results:
- Both HSMP and HSSVM methods demonstrated superior performance compared to existing approaches.
- Achieved high Area Under the ROC Curve (AUC) values of 0.981 and 0.984, respectively.
- Top-ranked predicted miRNA-disease associations were found to be reasonable and credible upon validation.
Conclusions:
- Multipath methods show significant promise in identifying novel miRNA-disease associations.
- These computational approaches can guide future biological experiments and research.
- The findings contribute to advancing the understanding of system genetics and disease mechanisms.
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