TY  - EJOU
AU  - Wang, Jiacheng
AU  - Zhang, Jingpu
AU  - Cai, Yideng
AU  - Deng, Lei
TI  - DeepMiR2GO: Inferring Functions of Human MicroRNAs Using a Deep Multi-Label Classification Model
T2  - International Journal of Molecular Sciences

PY  - 2019
VL  - 20
IS  - 23
SN  - 1422-0067

AB  - MicroRNAs (miRNAs) are a highly abundant collection of functional non-coding RNAs involved in cellular regulation and various complex human diseases. Although a large number of miRNAs have been identified, most of their physiological functions remain unknown. Computational methods play a vital role in exploring the potential functions of miRNAs. Here, we present DeepMiR2GO, a tool for integrating miRNAs, proteins and diseases, to predict the gene ontology (GO) functions based on multiple deep neuro-symbolic models. DeepMiR2GO starts by integrating the miRNA co-expression network, protein-protein interaction (PPI) network, disease phenotype similarity network, and interactions or associations among them into a global heterogeneous network. Then, it employs an efficient graph embedding strategy to learn potential network representations of the global heterogeneous network as the topological features. Finally, a deep multi-label classification network based on multiple neuro-symbolic models is built and used to annotate the GO terms of miRNAs. The predicted results demonstrate that DeepMiR2GO performs significantly better than other state-of-the-art approaches in terms of precision, recall, and maximum F-measure.
KW  - MicroRNA function
KW  - heterogeneous network
KW  - graph embedding
KW  - deep multi-label classification
DO  - 10.3390/ijms20236046