Email updates

Keep up to date with the latest news and content from BMC Medical Genomics and BioMed Central.

This article is part of the supplement: Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2012: Medical Genomics

Open Access Open Badges Research

Inferring drug-disease associations from integration of chemical, genomic and phenotype data using network propagation

Yu-Fen Huang1, Hsiang-Yuan Yeh2* and Von-Wun Soo12

Author Affiliations

1 Institute of Information Systems and Applications, National Tsing Hua University, HsinChu, Taiwan

2 Department of Computer Science, National Tsing Hua University, HsinChu, Taiwan

For all author emails, please log on.

BMC Medical Genomics 2013, 6(Suppl 3):S4  doi:10.1186/1755-8794-6-S3-S4

Published: 11 November 2013



During the last few years, the knowledge of drug, disease phenotype and protein has been rapidly accumulated and more and more scientists have been drawn the attention to inferring drug-disease associations by computational method. Development of an integrated approach for systematic discovering drug-disease associations by those informational data is an important issue.


We combine three different networks of drug, genomic and disease phenotype and assign the weights to the edges from available experimental data and knowledge. Given a specific disease, we use our network propagation approach to infer the drug-disease associations.


We apply prostate cancer and colorectal cancer as our test data. We use the manually curated drug-disease associations from comparative toxicogenomics database to be our benchmark. The ranked results show that our proposed method obtains higher specificity and sensitivity and clearly outperforms previous methods. Our result also show that our method with off-targets information gets higher performance than that with only primary drug targets in both test data.


We clearly demonstrate the feasibility and benefits of using network-based analyses of chemical, genomic and phenotype data to reveal drug-disease associations. The potential associations inferred by our method provide new perspectives for toxicogenomics and drug reposition evaluation.