Prediction of Potential Small Molecule-Associated MicroRNAs Using Graphlet Interaction
MicroRNAs (miRNAs) have been proved to be targeted by the small molecules recently, which made using small molecules to target miRNAs become a possible therapy for human diseases. Therefore, it is very meaningful to investigate the relationships between small molecules and miRNAs, which is still yet...
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doaj-95bd77681e1040ada0493a9faf2f79852020-11-25T01:42:57ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122018-10-01910.3389/fphar.2018.01152394684Prediction of Potential Small Molecule-Associated MicroRNAs Using Graphlet InteractionNa-Na Guan0Ya-Zhou Sun1Zhong Ming2Zhong Ming3Jian-Qiang Li4Xing Chen5College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, ChinaCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen, ChinaCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen, ChinaNational Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, ChinaCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen, ChinaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou, ChinaMicroRNAs (miRNAs) have been proved to be targeted by the small molecules recently, which made using small molecules to target miRNAs become a possible therapy for human diseases. Therefore, it is very meaningful to investigate the relationships between small molecules and miRNAs, which is still yet in the newly-developing stage. In this paper, we presented a prediction model of Graphlet Interaction based inference for Small Molecule-MiRNA Association prediction (GISMMA) by combining small molecule similarity network, miRNA similarity network and known small molecule-miRNA association network. This model described the complex relationship between two small molecules or between two miRNAs using graphlet interaction which consists of 28 isomers. The association score between a small molecule and a miRNA was calculated based on counting the numbers of graphlet interaction throughout the small molecule similarity network and the miRNA similarity network, respectively. Global and two types of local leave-one-out cross validation (LOOCV) as well as five-fold cross validation were implemented in two datasets to evaluate GISMMA. For Dataset 1, the AUCs are 0.9291 for global LOOCV, 0.9505, and 0.7702 for two local LOOCVs, 0.9263 ± 0.0026 for five-fold cross validation; for Dataset 2, the AUCs are 0.8203, 0.8640, 0.6591, and 0.8554 ± 0.0063, in turn. In case study for small molecules, 5-Fluorouracil, 17β-Estradiol and 5-Aza-2′-deoxycytidine, the numbers of top 50 miRNAs predicted by GISMMA and validated to be related to these three small molecules by experimental literatures are in turn 30, 29, and 25. Based on the results from cross validations and case studies, it is easy to realize the excellent performance of GISMMA.https://www.frontiersin.org/article/10.3389/fphar.2018.01152/fullsmall moleculemicroRNAassociation predictiongraphlet interactionsimilarity calculation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Na-Na Guan Ya-Zhou Sun Zhong Ming Zhong Ming Jian-Qiang Li Xing Chen |
spellingShingle |
Na-Na Guan Ya-Zhou Sun Zhong Ming Zhong Ming Jian-Qiang Li Xing Chen Prediction of Potential Small Molecule-Associated MicroRNAs Using Graphlet Interaction Frontiers in Pharmacology small molecule microRNA association prediction graphlet interaction similarity calculation |
author_facet |
Na-Na Guan Ya-Zhou Sun Zhong Ming Zhong Ming Jian-Qiang Li Xing Chen |
author_sort |
Na-Na Guan |
title |
Prediction of Potential Small Molecule-Associated MicroRNAs Using Graphlet Interaction |
title_short |
Prediction of Potential Small Molecule-Associated MicroRNAs Using Graphlet Interaction |
title_full |
Prediction of Potential Small Molecule-Associated MicroRNAs Using Graphlet Interaction |
title_fullStr |
Prediction of Potential Small Molecule-Associated MicroRNAs Using Graphlet Interaction |
title_full_unstemmed |
Prediction of Potential Small Molecule-Associated MicroRNAs Using Graphlet Interaction |
title_sort |
prediction of potential small molecule-associated micrornas using graphlet interaction |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Pharmacology |
issn |
1663-9812 |
publishDate |
2018-10-01 |
description |
MicroRNAs (miRNAs) have been proved to be targeted by the small molecules recently, which made using small molecules to target miRNAs become a possible therapy for human diseases. Therefore, it is very meaningful to investigate the relationships between small molecules and miRNAs, which is still yet in the newly-developing stage. In this paper, we presented a prediction model of Graphlet Interaction based inference for Small Molecule-MiRNA Association prediction (GISMMA) by combining small molecule similarity network, miRNA similarity network and known small molecule-miRNA association network. This model described the complex relationship between two small molecules or between two miRNAs using graphlet interaction which consists of 28 isomers. The association score between a small molecule and a miRNA was calculated based on counting the numbers of graphlet interaction throughout the small molecule similarity network and the miRNA similarity network, respectively. Global and two types of local leave-one-out cross validation (LOOCV) as well as five-fold cross validation were implemented in two datasets to evaluate GISMMA. For Dataset 1, the AUCs are 0.9291 for global LOOCV, 0.9505, and 0.7702 for two local LOOCVs, 0.9263 ± 0.0026 for five-fold cross validation; for Dataset 2, the AUCs are 0.8203, 0.8640, 0.6591, and 0.8554 ± 0.0063, in turn. In case study for small molecules, 5-Fluorouracil, 17β-Estradiol and 5-Aza-2′-deoxycytidine, the numbers of top 50 miRNAs predicted by GISMMA and validated to be related to these three small molecules by experimental literatures are in turn 30, 29, and 25. Based on the results from cross validations and case studies, it is easy to realize the excellent performance of GISMMA. |
topic |
small molecule microRNA association prediction graphlet interaction similarity calculation |
url |
https://www.frontiersin.org/article/10.3389/fphar.2018.01152/full |
work_keys_str_mv |
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