Drug repositioning based on triangularly balanced structure for tissue-specific diseases in incomplete interactome

Publication date: Available online 22 March 2017
Source:Artificial Intelligence in Medicine
Author(s): Liang Yu, Jin Zhao, Lin Gao
Finding new uses for existing drugs has become a new strategy for decades to treat more patients. Few traditional approaches consider the tissue specificities of diseases. Moreover, disease genes, drug targets and protein interaction (PPI) networks remain largely incomplete and the relationships between drugs and diseases conform to the triangularly balanced structure. Therefore, based on tissue specificities of diseases, we apply the triangularly balanced theory and the module distance defined for incomplete interaction networks to build drug-disease associations. Our method is named as TTMD (Tissue specificity, Triangle balance theory and Module Distance). Firstly, we combine three different drug similarity networks. Then, in the tissue-specific PPI network of a disease, we calculate its similarities with drugs using module distance. Finally, breast cancer and hepatocellular carcinoma (HCC) are taken as case studies. In the top-5% of predicted associations, 96.9% and 90.3% results match with known associations in Comparative Toxicogenomics Database (CTD) for breast cancer and hepatocellular carcinoma respectively. Clinical verification, literature mining and KEGG pathways enrichment analysis are further conducted for the top-5% newly predicted associations. Overall, TTMD is an effective approach for predicting new drug indications for tissue-specific diseases and provides potential values for the treatments of complex diseases.



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