NDAMDA: Network distance analysis for MiRNA-disease association prediction.
J Cell Mol Med
; 22(5): 2884-2895, 2018 05.
Article
em En
| MEDLINE
| ID: mdl-29532987
In recent years, microRNAs (miRNAs) are attracting an increasing amount of researchers' attention, as accumulating studies show that miRNAs play important roles in various basic biological processes and that dysregulation of miRNAs is connected with diverse human diseases, particularly cancers. However, the experimental methods to identify associations between miRNAs and diseases remain costly and laborious. In this study, we developed a computational method named Network Distance Analysis for MiRNA-Disease Association prediction (NDAMDA) which could effectively predict potential miRNA-disease associations. The highlight of this method was the use of not only the direct network distance between 2 miRNAs (diseases) but also their respective mean network distances to all other miRNAs (diseases) in the network. The model's reliable performance was certified by the AUC of 0.8920 in global leave-one-out cross-validation (LOOCV), 0.8062 in local LOOCV and the average AUCs of 0.8935 ± 0.0009 in fivefold cross-validation. Moreover, we applied NDAMDA to 3 different case studies to predict potential miRNAs related to breast neoplasms, lymphoma, oesophageal neoplasms, prostate neoplasms and hepatocellular carcinoma. Results showed that 86%, 72%, 86%, 86% and 84% of the top 50 predicted miRNAs were supported by experimental association evidence. Therefore, NDAMDA is a reliable method for predicting disease-related miRNAs.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Biologia Computacional
/
Predisposição Genética para Doença
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MicroRNAs
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Redes Reguladoras de Genes
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Estudos de Associação Genética
Tipo de estudo:
Prognostic_studies
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Risk_factors_studies
Limite:
Female
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Humans
Idioma:
En
Ano de publicação:
2018
Tipo de documento:
Article