Predicting miRNA-disease associations using a hybrid feature representation in the heterogeneous network
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....
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doaj-32e6fbe5dacb4560b04332a5e6273eb32021-04-02T12:53:02ZengBMCBMC Medical Genomics1755-87942020-10-0113S1011110.1186/s12920-020-00783-0Predicting miRNA-disease associations using a hybrid feature representation in the heterogeneous networkMinghui Liu0Jingyi Yang1Jiacheng Wang2Lei Deng3School of Computer Science and Engineering,Central South UniversitySchool of Computer Science and Engineering,Central South UniversitySchool of Computer Science and Engineering,Central South UniversitySchool of Computer Science and Engineering,Central South UniversityAbstract 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.http://link.springer.com/article/10.1186/s12920-020-00783-0miRNA-disease associationHeteSim measureDiffusion featureeXtreme gradient boosting |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Minghui Liu Jingyi Yang Jiacheng Wang Lei Deng |
spellingShingle |
Minghui Liu Jingyi Yang Jiacheng Wang Lei Deng Predicting miRNA-disease associations using a hybrid feature representation in the heterogeneous network BMC Medical Genomics miRNA-disease association HeteSim measure Diffusion feature eXtreme gradient boosting |
author_facet |
Minghui Liu Jingyi Yang Jiacheng Wang Lei Deng |
author_sort |
Minghui Liu |
title |
Predicting miRNA-disease associations using a hybrid feature representation in the heterogeneous network |
title_short |
Predicting miRNA-disease associations using a hybrid feature representation in the heterogeneous network |
title_full |
Predicting miRNA-disease associations using a hybrid feature representation in the heterogeneous network |
title_fullStr |
Predicting miRNA-disease associations using a hybrid feature representation in the heterogeneous network |
title_full_unstemmed |
Predicting miRNA-disease associations using a hybrid feature representation in the heterogeneous network |
title_sort |
predicting mirna-disease associations using a hybrid feature representation in the heterogeneous network |
publisher |
BMC |
series |
BMC Medical Genomics |
issn |
1755-8794 |
publishDate |
2020-10-01 |
description |
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. |
topic |
miRNA-disease association HeteSim measure Diffusion feature eXtreme gradient boosting |
url |
http://link.springer.com/article/10.1186/s12920-020-00783-0 |
work_keys_str_mv |
AT minghuiliu predictingmirnadiseaseassociationsusingahybridfeaturerepresentationintheheterogeneousnetwork AT jingyiyang predictingmirnadiseaseassociationsusingahybridfeaturerepresentationintheheterogeneousnetwork AT jiachengwang predictingmirnadiseaseassociationsusingahybridfeaturerepresentationintheheterogeneousnetwork AT leideng predictingmirnadiseaseassociationsusingahybridfeaturerepresentationintheheterogeneousnetwork |
_version_ |
1721567289023660032 |