Construct anticancer drug-drug correlation network

ABSTRACT

Network biology methods have been promising in linking diseases, genes, and drugs. In this study, we propose a novel computational method to construct drug-drug correlation networks, which consist of drug compounds as network graph nodes and correlated protein-drug profiles above a pre-determined threshold as network graph edges. This computational method is based on extensions of related work on identifying disease-specific proteins within protein-protein sub-networks, and mining protein-drug association profiles from biomedical literature. Our method provides a quantitative framework to compare how each drug compound from one therapeutic area is correlated with other drug compounds in other therapeutic areas, using a drug's protein-drug association profile mined from biomedical literature. We applied this method to the study of drug re-purposing and found that two breast cancer drugs "Mitomycin" and "Bleomycin" may be top drug candidates for treating pancreatic cancers.

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INDEX TERMS

Primary Classification:
  J. Computer Applications
  J.3 LIFE AND MEDICAL SCIENCES

Additional Classification:
  I. Computing Methodologies
  I.7 DOCUMENT AND TEXT PROCESSING

General Terms:
Algorithms, Languages, Measurement, Verification

Keywords:
DDC (drug-drug correlation), PDA (protein-drug association), PPI (protein-protein interaction), biomedical literature mining, breast cancer, pancreatic cancer