A heterogeneous label propagation approach to explore the potential associations between miRNA and disease

Background Research on microRNAs (miRNAs) has attracted increasingly worldwide attention over recent years as growing experimental results have made clear that miRNA correlates with masses of critical biological processes and the occurrence, development, and diagnosis of human complex diseases. None...

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Vydáno v:Journal of translational medicine Ročník 16; číslo 1; s. 348 - 14
Hlavní autoři: Chen, Xing, Zhang, De-Hong, You, Zhu-Hong
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 11.12.2018
BioMed Central Ltd
Springer Nature B.V
BMC
Témata:
Algorithms
Bioinformatics
Biomedical and Life Sciences
Biomedicine
Breast cancer
Chemotherapy
Computational Biology - methods
Computer applications
Disease
Drug resistance
Esophageal cancer
Esophagus
Gene expression
Genetic Association Studies
Genetic disorders
Genetic Predisposition to Disease
Genomes
Genomics
Humans
Label propagation
Lung cancer
Lymphoma
Medical bioinformatics
Medical prognosis
Medical research
Medicine/Public Health
Methods
MicroRNA
MicroRNAs
MicroRNAs - genetics
MicroRNAs - metabolism
miRNA
miRNA-disease association
Multi-network
Neoplasms - genetics
Non-coding RNA
Propagation
Proteins
Reproducibility of Results
Research methodology
Risk factors
Signal transduction
Similarity measures
Tumors
miRNA
Multi-network
miRNA-disease association
Disease
Label propagation
ISSN:1479-5876, 1479-5876
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Abstract Background Research on microRNAs (miRNAs) has attracted increasingly worldwide attention over recent years as growing experimental results have made clear that miRNA correlates with masses of critical biological processes and the occurrence, development, and diagnosis of human complex diseases. Nonetheless, the known miRNA-disease associations are still insufficient considering plenty of human miRNAs discovered now. Therefore, there is an urgent need for effective computational model predicting novel miRNA-disease association prediction to save time and money for follow-up biological experiments. Methods In this study, considering the insufficiency of the previous computational methods, we proposed the model named heterogeneous label propagation for MiRNA-disease association prediction (HLPMDA), in which a heterogeneous label was propagated on the multi-network of miRNA, disease and long non-coding RNA (lncRNA) to infer the possible miRNA-disease association. The strength of the data about lncRNA–miRNA association and lncRNA-disease association enabled HLPMDA to produce a better prediction. Results HLPMDA achieved AUCs of 0.9232, 0.8437 and 0.9218 ± 0.0004 based on global and local leave-one-out cross validation and 5-fold cross validation, respectively. Furthermore, three kinds of case studies were implemented and 47 (esophageal neoplasms), 49 (breast neoplasms) and 46 (lymphoma) of top 50 candidate miRNAs were proved by experiment reports. Conclusions All the results adequately showed that HLPMDA is a recommendable miRNA-disease association prediction method. We anticipated that HLPMDA could help the follow-up investigations by biomedical researchers.
AbstractList Abstract Background Research on microRNAs (miRNAs) has attracted increasingly worldwide attention over recent years as growing experimental results have made clear that miRNA correlates with masses of critical biological processes and the occurrence, development, and diagnosis of human complex diseases. Nonetheless, the known miRNA-disease associations are still insufficient considering plenty of human miRNAs discovered now. Therefore, there is an urgent need for effective computational model predicting novel miRNA-disease association prediction to save time and money for follow-up biological experiments. Methods In this study, considering the insufficiency of the previous computational methods, we proposed the model named heterogeneous label propagation for MiRNA-disease association prediction (HLPMDA), in which a heterogeneous label was propagated on the multi-network of miRNA, disease and long non-coding RNA (lncRNA) to infer the possible miRNA-disease association. The strength of the data about lncRNA–miRNA association and lncRNA-disease association enabled HLPMDA to produce a better prediction. Results HLPMDA achieved AUCs of 0.9232, 0.8437 and 0.9218 ± 0.0004 based on global and local leave-one-out cross validation and 5-fold cross validation, respectively. Furthermore, three kinds of case studies were implemented and 47 (esophageal neoplasms), 49 (breast neoplasms) and 46 (lymphoma) of top 50 candidate miRNAs were proved by experiment reports. Conclusions All the results adequately showed that HLPMDA is a recommendable miRNA-disease association prediction method. We anticipated that HLPMDA could help the follow-up investigations by biomedical researchers.
Research on microRNAs (miRNAs) has attracted increasingly worldwide attention over recent years as growing experimental results have made clear that miRNA correlates with masses of critical biological processes and the occurrence, development, and diagnosis of human complex diseases. Nonetheless, the known miRNA-disease associations are still insufficient considering plenty of human miRNAs discovered now. Therefore, there is an urgent need for effective computational model predicting novel miRNA-disease association prediction to save time and money for follow-up biological experiments. In this study, considering the insufficiency of the previous computational methods, we proposed the model named heterogeneous label propagation for MiRNA-disease association prediction (HLPMDA), in which a heterogeneous label was propagated on the multi-network of miRNA, disease and long non-coding RNA (lncRNA) to infer the possible miRNA-disease association. The strength of the data about lncRNA-miRNA association and lncRNA-disease association enabled HLPMDA to produce a better prediction. HLPMDA achieved AUCs of 0.9232, 0.8437 and 0.9218 ± 0.0004 based on global and local leave-one-out cross validation and 5-fold cross validation, respectively. Furthermore, three kinds of case studies were implemented and 47 (esophageal neoplasms), 49 (breast neoplasms) and 46 (lymphoma) of top 50 candidate miRNAs were proved by experiment reports. All the results adequately showed that HLPMDA is a recommendable miRNA-disease association prediction method. We anticipated that HLPMDA could help the follow-up investigations by biomedical researchers.
Background Research on microRNAs (miRNAs) has attracted increasingly worldwide attention over recent years as growing experimental results have made clear that miRNA correlates with masses of critical biological processes and the occurrence, development, and diagnosis of human complex diseases. Nonetheless, the known miRNA-disease associations are still insufficient considering plenty of human miRNAs discovered now. Therefore, there is an urgent need for effective computational model predicting novel miRNA-disease association prediction to save time and money for follow-up biological experiments. Methods In this study, considering the insufficiency of the previous computational methods, we proposed the model named heterogeneous label propagation for MiRNA-disease association prediction (HLPMDA), in which a heterogeneous label was propagated on the multi-network of miRNA, disease and long non-coding RNA (lncRNA) to infer the possible miRNA-disease association. The strength of the data about lncRNA-miRNA association and lncRNA-disease association enabled HLPMDA to produce a better prediction. Results HLPMDA achieved AUCs of 0.9232, 0.8437 and 0.9218 [+ or -] 0.0004 based on global and local leave-one-out cross validation and 5-fold cross validation, respectively. Furthermore, three kinds of case studies were implemented and 47 (esophageal neoplasms), 49 (breast neoplasms) and 46 (lymphoma) of top 50 candidate miRNAs were proved by experiment reports. Conclusions All the results adequately showed that HLPMDA is a recommendable miRNA-disease association prediction method. We anticipated that HLPMDA could help the follow-up investigations by biomedical researchers. Keywords: miRNA, Disease, miRNA-disease association, Multi-network, Label propagation
Research on microRNAs (miRNAs) has attracted increasingly worldwide attention over recent years as growing experimental results have made clear that miRNA correlates with masses of critical biological processes and the occurrence, development, and diagnosis of human complex diseases. Nonetheless, the known miRNA-disease associations are still insufficient considering plenty of human miRNAs discovered now. Therefore, there is an urgent need for effective computational model predicting novel miRNA-disease association prediction to save time and money for follow-up biological experiments.BACKGROUNDResearch on microRNAs (miRNAs) has attracted increasingly worldwide attention over recent years as growing experimental results have made clear that miRNA correlates with masses of critical biological processes and the occurrence, development, and diagnosis of human complex diseases. Nonetheless, the known miRNA-disease associations are still insufficient considering plenty of human miRNAs discovered now. Therefore, there is an urgent need for effective computational model predicting novel miRNA-disease association prediction to save time and money for follow-up biological experiments.In this study, considering the insufficiency of the previous computational methods, we proposed the model named heterogeneous label propagation for MiRNA-disease association prediction (HLPMDA), in which a heterogeneous label was propagated on the multi-network of miRNA, disease and long non-coding RNA (lncRNA) to infer the possible miRNA-disease association. The strength of the data about lncRNA-miRNA association and lncRNA-disease association enabled HLPMDA to produce a better prediction.METHODSIn this study, considering the insufficiency of the previous computational methods, we proposed the model named heterogeneous label propagation for MiRNA-disease association prediction (HLPMDA), in which a heterogeneous label was propagated on the multi-network of miRNA, disease and long non-coding RNA (lncRNA) to infer the possible miRNA-disease association. The strength of the data about lncRNA-miRNA association and lncRNA-disease association enabled HLPMDA to produce a better prediction.HLPMDA achieved AUCs of 0.9232, 0.8437 and 0.9218 ± 0.0004 based on global and local leave-one-out cross validation and 5-fold cross validation, respectively. Furthermore, three kinds of case studies were implemented and 47 (esophageal neoplasms), 49 (breast neoplasms) and 46 (lymphoma) of top 50 candidate miRNAs were proved by experiment reports.RESULTSHLPMDA achieved AUCs of 0.9232, 0.8437 and 0.9218 ± 0.0004 based on global and local leave-one-out cross validation and 5-fold cross validation, respectively. Furthermore, three kinds of case studies were implemented and 47 (esophageal neoplasms), 49 (breast neoplasms) and 46 (lymphoma) of top 50 candidate miRNAs were proved by experiment reports.All the results adequately showed that HLPMDA is a recommendable miRNA-disease association prediction method. We anticipated that HLPMDA could help the follow-up investigations by biomedical researchers.CONCLUSIONSAll the results adequately showed that HLPMDA is a recommendable miRNA-disease association prediction method. We anticipated that HLPMDA could help the follow-up investigations by biomedical researchers.
Background Research on microRNAs (miRNAs) has attracted increasingly worldwide attention over recent years as growing experimental results have made clear that miRNA correlates with masses of critical biological processes and the occurrence, development, and diagnosis of human complex diseases. Nonetheless, the known miRNA-disease associations are still insufficient considering plenty of human miRNAs discovered now. Therefore, there is an urgent need for effective computational model predicting novel miRNA-disease association prediction to save time and money for follow-up biological experiments. Methods In this study, considering the insufficiency of the previous computational methods, we proposed the model named heterogeneous label propagation for MiRNA-disease association prediction (HLPMDA), in which a heterogeneous label was propagated on the multi-network of miRNA, disease and long non-coding RNA (lncRNA) to infer the possible miRNA-disease association. The strength of the data about lncRNA–miRNA association and lncRNA-disease association enabled HLPMDA to produce a better prediction. Results HLPMDA achieved AUCs of 0.9232, 0.8437 and 0.9218 ± 0.0004 based on global and local leave-one-out cross validation and 5-fold cross validation, respectively. Furthermore, three kinds of case studies were implemented and 47 (esophageal neoplasms), 49 (breast neoplasms) and 46 (lymphoma) of top 50 candidate miRNAs were proved by experiment reports. Conclusions All the results adequately showed that HLPMDA is a recommendable miRNA-disease association prediction method. We anticipated that HLPMDA could help the follow-up investigations by biomedical researchers.
In this study, considering the insufficiency of the previous computational methods, we proposed the model named heterogeneous label propagation for MiRNA-disease association prediction (HLPMDA), in which a heterogeneous label was propagated on the multi-network of miRNA, disease and long non-coding RNA (lncRNA) to infer the possible miRNA-disease association. The strength of the data about lncRNA-miRNA association and lncRNA-disease association enabled HLPMDA to produce a better prediction. HLPMDA achieved AUCs of 0.9232, 0.8437 and 0.9218 [+ or -] 0.0004 based on global and local leave-one-out cross validation and 5-fold cross validation, respectively. Furthermore, three kinds of case studies were implemented and 47 (esophageal neoplasms), 49 (breast neoplasms) and 46 (lymphoma) of top 50 candidate miRNAs were proved by experiment reports. All the results adequately showed that HLPMDA is a recommendable miRNA-disease association prediction method. We anticipated that HLPMDA could help the follow-up investigations by biomedical researchers.
Background Research on microRNAs (miRNAs) has attracted increasingly worldwide attention over recent years as growing experimental results have made clear that miRNA correlates with masses of critical biological processes and the occurrence, development, and diagnosis of human complex diseases. Nonetheless, the known miRNA-disease associations are still insufficient considering plenty of human miRNAs discovered now. Therefore, there is an urgent need for effective computational model predicting novel miRNA-disease association prediction to save time and money for follow-up biological experiments. Methods In this study, considering the insufficiency of the previous computational methods, we proposed the model named heterogeneous label propagation for MiRNA-disease association prediction (HLPMDA), in which a heterogeneous label was propagated on the multi-network of miRNA, disease and long non-coding RNA (lncRNA) to infer the possible miRNA-disease association. The strength of the data about lncRNA–miRNA association and lncRNA-disease association enabled HLPMDA to produce a better prediction. Results HLPMDA achieved AUCs of 0.9232, 0.8437 and 0.9218 ± 0.0004 based on global and local leave-one-out cross validation and 5-fold cross validation, respectively. Furthermore, three kinds of case studies were implemented and 47 (esophageal neoplasms), 49 (breast neoplasms) and 46 (lymphoma) of top 50 candidate miRNAs were proved by experiment reports. Conclusions All the results adequately showed that HLPMDA is a recommendable miRNA-disease association prediction method. We anticipated that HLPMDA could help the follow-up investigations by biomedical researchers.
ArticleNumber 348
Audience Academic
Author You, Zhu-Hong
Zhang, De-Hong
Chen, Xing
Author_xml – sequence: 1
  givenname: Xing
  surname: Chen
  fullname: Chen, Xing
  email: xingchen@amss.ac.cn
  organization: School of Information and Control Engineering, China University of Mining and Technology
– sequence: 2
  givenname: De-Hong
  surname: Zhang
  fullname: Zhang, De-Hong
  organization: School of Information and Control Engineering, China University of Mining and Technology
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  givenname: Zhu-Hong
  surname: You
  fullname: You, Zhu-Hong
  email: zhuhongyou@ms.xjb.ac.cn
  organization: Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science
BackLink https://www.ncbi.nlm.nih.gov/pubmed/30537965$$D View this record in MEDLINE/PubMed
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Issue 1
Keywords miRNA
Multi-network
miRNA-disease association
Disease
Label propagation
Language English
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Snippet Background Research on microRNAs (miRNAs) has attracted increasingly worldwide attention over recent years as growing experimental results have made clear that...
Research on microRNAs (miRNAs) has attracted increasingly worldwide attention over recent years as growing experimental results have made clear that miRNA...
Background Research on microRNAs (miRNAs) has attracted increasingly worldwide attention over recent years as growing experimental results have made clear that...
In this study, considering the insufficiency of the previous computational methods, we proposed the model named heterogeneous label propagation for...
Abstract Background Research on microRNAs (miRNAs) has attracted increasingly worldwide attention over recent years as growing experimental results have made...
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StartPage 348
SubjectTerms Algorithms
Bioinformatics
Biomedical and Life Sciences
Biomedicine
Breast cancer
Chemotherapy
Computational Biology - methods
Computer applications
Disease
Drug resistance
Esophageal cancer
Esophagus
Gene expression
Genetic Association Studies
Genetic disorders
Genetic Predisposition to Disease
Genomes
Genomics
Humans
Label propagation
Lung cancer
Lymphoma
Medical bioinformatics
Medical prognosis
Medical research
Medicine/Public Health
Methods
MicroRNA
MicroRNAs
MicroRNAs - genetics
MicroRNAs - metabolism
miRNA
miRNA-disease association
Multi-network
Neoplasms - genetics
Non-coding RNA
Propagation
Proteins
Reproducibility of Results
Research methodology
Risk factors
Signal transduction
Similarity measures
Tumors
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Title A heterogeneous label propagation approach to explore the potential associations between miRNA and disease
URI https://link.springer.com/article/10.1186/s12967-018-1722-1
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Volume 16
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