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Open AccessArticle

DeepMiR2GO: Inferring Functions of Human MicroRNAs Using a Deep Multi-Label Classification Model

1
School of Computer Science and Engineering, Central South University, Changsha 410083, China
2
School of Computer and Data Science, Henan University of Urban Construction, Pingdingshan 467000, China
3
School of Software, Xinjiang University, Urumqi 830008, China
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2019, 20(23), 6046; https://doi.org/10.3390/ijms20236046
Received: 26 October 2019 / Revised: 25 November 2019 / Accepted: 26 November 2019 / Published: 30 November 2019
(This article belongs to the Special Issue Special Protein or RNA Molecules Computational Identification 2019)
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. View Full-Text
Keywords: MicroRNA function; heterogeneous network; graph embedding; deep multi-label classification MicroRNA function; heterogeneous network; graph embedding; deep multi-label classification
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Wang, J.; Zhang, J.; Cai, Y.; Deng, L. DeepMiR2GO: Inferring Functions of Human MicroRNAs Using a Deep Multi-Label Classification Model. Int. J. Mol. Sci. 2019, 20, 6046.

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