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Construct anticancer drug-drug correlation network

Published: 08 March 2009 Publication History

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.

References

[1]
Imming, P., Sinning, C., and Meyer, A., Drugs, their targets and the nature and number of drug targets. Nature reviews Drug discovery, 2006. 5(10): p. 821--34.
[2]
Yildirim, M. A., et al., Drug-target network. Nature biotechnology, 2007. 25(10): p. 1119--26.
[3]
Goh, K. I., et al., The human disease network. Proceedings of the National Academy of Sciences of the United States of America, 2007. 104(21): p. 8685--8690.
[4]
Hopkins, A. L., Network pharmacology. Nature biotechnology, 2007. 25(10): p. 1110--1.
[5]
O'Connor, K. A. and Roth, B. L., Finding new tricks for old drugs: an efficient route for public-sector drug discovery. Nature reviews Drug discovery, 2005. 4(12): p. 1005--14.
[6]
American Cancer Society, Cancer Facts & Figures 2008, Atlanta: American Cancer Society; 2008.
[7]
Laheru, D. and Jaffee, E. M., Immunotherapy for pancreatic cancer - science driving clinical progress. Nature reviews Cancer, 2005. 5(6): p. 459--67.
[8]
Li, J., Zhu, X., and Chen, J. Y. Mining Disease-Specific Molecular Association Profiles from Biomedical Literature: A Case Study. in ACM Symposium on Applied Computing 2008. 2008. Fortaleza, Brazil.
[9]
Chen, J. Y., Shen, C., and Sivachenko, A. Y., Mining Alzheimer disease relevant proteins from integrated protein interactome data. Pacific Symposium on Biocomputing Pacific Symposium on Biocomputing, 2006: p. 367--378.
[10]
Hamosh, A., et al., Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic acids research, 2005. 33(Database issue): p. D514--517.
[11]
HAPPI: Human Annotated and Predicted Protein Interaction. http://discern.uits.iu.edu:8340/HAPPI/index.html.
[12]
Li, J., et al. Learning Domain-Specific Knowledge from Context--THUIR at TREC2005 Genomics Track. in Proceedings of 14th Text Retrireval Conference (TREC2005). 2005. Gaithersburg, USA.
[13]
MeSH: Medical Subject Headings (acess 2007). http://www.nlm.nih.gov/mesh/.
[14]
Wu, C. H., et al., The Universal Protein Resource (UniProt): an expanding universe of protein information. Nucleic acids research, 2006. 34(Database issue): p. D187--91.
[15]
Huan, T., et al., ProteoLens: a visual analytic tool for multi-scale database-driven biological network data mining. BMC Bioinformatics, 2008. 9(Suppl 9): p. S5.
[16]
2008 MEDLINE/PubMed Baseline Data. http://www.nlm.nih.gov/bsd/licensee/2008_stats/baseline_doc.htm l.
[17]
Mitomycin in Clinical Trials. http://clinicaltrials.gov/ct2/show/record/NCT00386399?term=Mit omycins&rank=11.
[18]
Bleomycin in ClinicalTrhials http://clinicaltrials.gov/ct2/show/record/NCT00027521?term=Bleomycin&rank=14.

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  1. Construct anticancer drug-drug correlation network

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      cover image ACM Conferences
      SAC '09: Proceedings of the 2009 ACM symposium on Applied Computing
      March 2009
      2347 pages
      ISBN:9781605581668
      DOI:10.1145/1529282
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      Published: 08 March 2009

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      Author Tags

      1. DDC (drug-drug correlation)
      2. PDA (protein-drug association)
      3. PPI (protein-protein interaction)
      4. biomedical literature mining
      5. breast cancer
      6. pancreatic cancer

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      March 8, 2009 - March 12, 2008
      Hawaii, Honolulu

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