CODA: Integrating multi-level context-oriented directed associations for analysis of drug effects

In silico network-based methods have shown promising results in the field of drug development. Yet, most of networks used in the previous research have not included context information even though biological associations actually do appear in the specific contexts. Here, we reconstruct an anatomical context-specific network by assigning contexts to biological associations using protein expression data and scientific literature. Furthermore, we employ the context-specific network for the analysis of drug effects with a proximity measure between drug targets and diseases. Distinct from previous context-specific networks, intercellular associations and phenomic level entities such as biological processes are included in our network to represent the human body. It is observed that performances in inferring drug-disease associations are increased by adding context information and phenomic level entities. In particular, hypertension, a disease related to multiple organs and associated with several phenomic level entities, is analyzed in detail to investigate how our network facilitates the inference of drug-disease associations. Our results indicate that the inclusion of context information, intercellular associations, and phenomic level entities can contribute towards a better prediction of drug-disease associations and provide detailed insight into understanding of how drugs affect diseases in the human body.
Publisher
NATURE PUBLISHING GROUP
Issue Date
2017-08
Language
English
Keywords

BLOOD-PRESSURE; HYPERTENSION; TISSUE; DATABASE; NEBIVOLOL; NETWORKS; DISEASE; SYSTEM; RENIN; GENE

Citation

SCIENTIFIC REPORTS, v.7, pp.7519

ISSN
2045-2322
DOI
10.1038/s41598-017-07448-6
URI
http://hdl.handle.net/10203/225611
Appears in Collection
BiS-Journal Papers(저널논문)
Files in This Item
000407180200020.pdf(1.66 MB)Download
  • Hit : 114
  • Download : 24
  • Cited 0 times in thomson ci

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0