Exploiting Literature-derived Knowledge and Semantics to Identify Potential Prostate Cancer Drugs

In this study, we report on the performance of an automated approach to discovery of potential prostate cancer drugs from the biomedical literature. We used the semantic relationships in SemMedDB, a database of structured knowledge extracted from all MEDLINE citations using SemRep, to extract potent...

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Bibliographic Details
Published in:Cancer informatics 2014-01, Vol.2014 (Suppl. 1), p.103-111
Main Authors: Zhang, Rui, Cairelli, Michael J., Fiszman, Marcelo, Kilicoglu, Halil, Rindflesch, Thomas C., Pakhomov, Serguei V., Melton, Genevieve B.
Format: Article
Language:English
Subjects:
Antimitotic agents
Antineoplastic agents
Computational linguistics
Drug therapy
Health aspects
Language processing
Natural language interfaces
Original Research
Prostate cancer
Semantics
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Summary:In this study, we report on the performance of an automated approach to discovery of potential prostate cancer drugs from the biomedical literature. We used the semantic relationships in SemMedDB, a database of structured knowledge extracted from all MEDLINE citations using SemRep, to extract potential relationships using knowledge of cancer drugs pathways. Two cancer drugs pathway schemas were constructed using these relationships extracted from SemMedDB. Trough both pathway schemas, we found drugs already used for prostate cancer therapy and drugs not currently listed as the prostate cancer medications. Our study demonstrates that the appropriate linking of relevant structured semantic relationships stored in SemMedDB can support the discovery of potential prostate cancer drugs.
ISSN:1176-9351
1176-9351
DOI:10.4137/CIN.S13889