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Predicting Gene Ontology Function of Human MicroRNAs by Integrating Multiple Networks
Lei Deng1, Jiacheng Wang1, Jingpu Zhang2
1School of Software, Central South University, Changsha, China.
Frontiers in Genetics
|February 15, 2019
Summary
We developed PmiRGO, an integrated model to predict microRNA (miRNA) functions using gene ontology (GO) terms. PmiRGO outperforms existing methods, aiding in understanding molecular mechanisms of human diseases.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- MicroRNAs (miRNAs) are crucial regulators of human biological processes.
- Understanding miRNA functions is key to elucidating molecular mechanisms of diseases.
Purpose of the Study:
- To develop an integrated computational model, PmiRGO, for inferring gene ontology (GO) functions of miRNAs.
- To improve the accuracy of miRNA function prediction by integrating diverse biological data.
Main Methods:
- PmiRGO integrates miRNA expression profiles, miRNA-target interactions, and protein-protein interaction (PPI) networks.
- It constructs a global heterogeneous network and uses DeepWalk for latent representation learning.
- Support Vector Machine (SVM) models are employed for GO term annotation.
Main Results:
- PmiRGO demonstrates significantly superior performance compared to state-of-the-art methods, indicated by improved F-score.
- A case study validates the model's capability in annotating potential miRNA functions.
Conclusions:
- PmiRGO provides an effective approach for predicting miRNA functions.
- The model contributes to a deeper understanding of miRNA roles in human biological processes and disease pathogenesis.
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