A co-module approach for elucidating drug-disease associations and revealing their molecular basis.
Bioinformatics
; 28(7): 955-61, 2012 Apr 01.
Article
em En
| MEDLINE
| ID: mdl-22285830
ABSTRACT
MOTIVATION Understanding how drugs and diseases are associated in the molecular level is of critical importance to unveil disease mechanisms and treatments. Until recently, few studies attempt end to discover important gene modules shared by both drugs and diseases. RESULTS:
Here, we propose a novel presentation of drug-gene-disease relationship, a 'co-module', which is characterized by closely related drugs, diseases and genes. We first define a network-based gene closeness profile to relate drug to disease. Then, we develop a Bayesian partition method to identify drug-gene-disease co-modules underlying the gene closeness data. Genes share similar notable patterns with respect not only to the drugs but also the diseases within a co-module. Simulations show that our method, comCIPHER, achieves a better performance compared with a popular co-module detection method, PPA. We apply comCIPHER to a set consisting of 723 drugs, 275 diseases and 1442 genes and demonstrate that our co-module approach is able to identify new drug-disease associations and highlight their molecular basis. Disease co-morbidity emerges as well. Three co-modules are further illustrated in which new drug applications, including the anti-cancer metastasis activity of an anti-asthma drug Pranlukast, and a cardiovascular stress-testing agent Arbutamine for obesity, as well as potential side-effects, e.g. hypotension for Triamterene, are computationally identified.AVAILABILITY:
The compiled version of comCIPHER can be found at http//bioinfo.au.tsinghua.edu.cn/comCIPHER/. The 86 co-modules can be downloaded from http//bioinfo.au.tsinghua.edu.cn/comCIPHER/Co_Module_Results.zip. CONTACT shaoli@mail.tsinghua.edu.cn SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Texto completo:
1
Coleções:
01-internacional
Contexto em Saúde:
1_ASSA2030
Base de dados:
MEDLINE
Assunto principal:
Biologia Computacional
/
Suscetibilidade a Doenças
/
Tratamento Farmacológico
/
Redes Reguladoras de Genes
Tipo de estudo:
Health_economic_evaluation
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Bioinformatics
Ano de publicação:
2012
Tipo de documento:
Article