Predicting miRNA-disease associations using a hybrid feature representation in the heterogeneous network
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RESEARCH
Open Access
Predicting miRNA-disease associations using a hybrid feature representation in the heterogeneous network Minghui Liu1† , Jingyi Yang1† , Jiacheng Wang1 and Lei Deng1,2* From The 18th Asia Pacific Bioinformatics Conference Seoul, Korea. 18-20 August 2020
Abstract Background: Studies have found that miRNAs play an important role in many biological activities involved in human diseases. Revealing the associations between miRNA and disease by biological experiments is time-consuming and expensive. The computational approaches provide a new alternative. However, because of the limited knowledge of the associations between miRNAs and diseases, it is difficult to support the prediction model effectively. Methods: In this work, we propose a model to predict miRNA-disease associations, MDAPCOM, in which protein information associated with miRNAs and diseases is introduced to build a global miRNA-protein-disease network. Subsequently, diffusion features and HeteSim features, extracted from the global network, are combined to train the prediction model by eXtreme Gradient Boosting (XGBoost). Results: The MDAPCOM model achieves AUC of 0.991 based on 10-fold cross-validation, which is significantly better than that of other two state-of-the-art methods RWRMDA and PRINCE. Furthermore, the model performs well on three unbalanced data sets. Conclusions: The results suggest that the information behind proteins associated with miRNAs and diseases is crucial to the prediction of the associations between miRNAs and diseases, and the hybrid feature representation in the heterogeneous network is very effective for improving predictive performance. Keywords: miRNA-disease association, HeteSim measure, Diffusion feature, eXtreme gradient boosting
Background MicroRNAs(miRNAs) are a kind of small single-stranded endogenous non-coding RNAs with a length about 22 nucleotides, which play an important role in regulating the gene expression during the post-transcriptional level [1, 2]. Many studies have shown that the dysregulation of miRNAs is involved in multiple human diseases like *Correspondence: [email protected] † Minghui Liu and Jingyi Yang contributed equally to this work. School of Computer Science and Engineering,Central South University, 410075, Changsha, China 2 School of Software, Xinjiang University, 830008, Urumqi, China 1
cancers [3], cardiovascular diseases [4] and Alzheimer’s diseases [5] etc., and the prediction of miRNAs-diseases associations is crucial to understand the diseases pathogenesis [6]. Furthermore, George Adrian, et al. found that the miR15 and miR16 are deleted in a lot B cell chronic lymphocytic leukemias (B-CLL) [7], T. Sredni et al. demonstrated that miR-129 and miR-25 express abnormally in all pediatric brain tumor types [8]. Besides, Jun Lu et al. successfully classified poorly differentiated tumours using miRNA expression profiles [9], which demonstrated the potential of miRNAs as biomarkers. Therefore, Pre-
© The Author(s). 2020 Open Access This article is licensed under a Crea
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