Prediction and validation of association between microRNAs and diseases by multipath methods.

Zeng, X; Zhang, X; Liao, Y; Pan, L

Prediction and validation of association between microRNAs and diseases by multipath methods.

Zeng, X Zhang, X

Liao, Y Pan, L

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Biochim Biophys Acta, 2016, 1860, (11 Pt B), pp. 2735-2739
Issue Date:: 2016-11

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Field	Value	Language
dc.contributor.author	Zeng, X
dc.contributor.author	Zhang, X https://orcid.org/0000-0002-3089-9809
dc.contributor.author	Liao, Y
dc.contributor.author	Pan, L
dc.date.accessioned	2022-10-26T06:18:54Z
dc.date.available	2016-03-10
dc.date.available	2022-10-26T06:18:54Z
dc.date.issued	2016-11
dc.identifier.citation	Biochim Biophys Acta, 2016, 1860, (11 Pt B), pp. 2735-2739
dc.identifier.issn	0006-3002
dc.identifier.issn	1878-2434
dc.identifier.uri	http://hdl.handle.net/10453/162724
dc.description.abstract	BACKGROUND: Deciphering the genetic basis of human diseases is an important goal in biomedical research. There is increasing evidence suggesting that microRNAs play critical roles in many key biological processes. So the identification of microRNAs associated with disease is very important for understanding the pathogenesis of diseases. METHODS: Two multipath methods are introduced to predict the associations between microRNAs and diseases based on microRNA-disease heterogeneous network. The first method, HeteSim_MultiPath (HSMP), uses the HeteSim measure to calculate the similarity between objects and combines the HeteSim scores of different paths with a constant that dampens the contributions of longer paths. The second one, HeteSim_SVM (HSSVM), uses the HeteSim measure and the machine learning method used to combine HeteSim scores instead of a constant. RESULTS: We use the leave-one-out cross-validation to evaluate our novel methods, and find that our methods are better than other methods. We achieve an area under the ROC curve of 0.981 and 0.984 respectively. We also check the top-10 most similarity of microRNAs-diseases associations and find that our predictions are reasonable and credible. CONCLUSIONS: The encouraging results suggest that multipath methods can provide help in identifying novel microRNA-disease associations, and guide biological experiments for scientific research. This article is part of a Special Issue entitled "System Genetics". Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	ELSEVIER
dc.relation.ispartof	Biochim Biophys Acta
dc.relation.isbasedon	10.1016/j.bbagen.2016.03.016
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	02 Physical Sciences, 06 Biological Sciences
dc.subject.mesh	Algorithms
dc.subject.mesh	Computational Biology
dc.subject.mesh	Disease
dc.subject.mesh	Humans
dc.subject.mesh	MicroRNAs
dc.subject.mesh	ROC Curve
dc.subject.mesh	Humans
dc.subject.mesh	Disease
dc.subject.mesh	MicroRNAs
dc.subject.mesh	ROC Curve
dc.subject.mesh	Computational Biology
dc.subject.mesh	Algorithms
dc.title	Prediction and validation of association between microRNAs and diseases by multipath methods.
dc.type	Journal Article
utslib.citation.volume	1860
utslib.location.activity	Netherlands
utslib.for	02 Physical Sciences
utslib.for	06 Biological Sciences
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2022-10-26T06:18:53Z
pubs.issue	11 Pt B
pubs.publication-status	Published
pubs.volume	1860
utslib.citation.issue	11 Pt B

Abstract:

BACKGROUND: Deciphering the genetic basis of human diseases is an important goal in biomedical research. There is increasing evidence suggesting that microRNAs play critical roles in many key biological processes. So the identification of microRNAs associated with disease is very important for understanding the pathogenesis of diseases. METHODS: Two multipath methods are introduced to predict the associations between microRNAs and diseases based on microRNA-disease heterogeneous network. The first method, HeteSim_MultiPath (HSMP), uses the HeteSim measure to calculate the similarity between objects and combines the HeteSim scores of different paths with a constant that dampens the contributions of longer paths. The second one, HeteSim_SVM (HSSVM), uses the HeteSim measure and the machine learning method used to combine HeteSim scores instead of a constant. RESULTS: We use the leave-one-out cross-validation to evaluate our novel methods, and find that our methods are better than other methods. We achieve an area under the ROC curve of 0.981 and 0.984 respectively. We also check the top-10 most similarity of microRNAs-diseases associations and find that our predictions are reasonable and credible. CONCLUSIONS: The encouraging results suggest that multipath methods can provide help in identifying novel microRNA-disease associations, and guide biological experiments for scientific research. This article is part of a Special Issue entitled "System Genetics". Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

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http://hdl.handle.net/10453/162724