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

Zhang R, Cairelli MJ, Fiszman M, Kilicoglu H, Rindflesch TC, Pakhomov SV, Melton GB - Cancer Inform (2014)

Bottom Line: 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.Through both pathway schemas, we found drugs already used for prostate cancer therapy and drugs not currently listed as the prostate cancer medications.

View Article: PubMed Central - PubMed

Affiliation: Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA. ; Department of Surgery, University of Minnesota, Minneapolis, MN, USA.

ABSTRACT
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. Through 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.

No MeSH data available.


Related in: MedlinePlus

(A) Two pathway schemas are utilized. The first connects a drug–gene predication with a gene–cancer predication and the second connects a drug–gene predication to a gene–gene predication and then the object gene of the gene–gene predication to a gene–cancer predication. (B) Drug–gene, gene–cancer, and gene–gene predications are all retrieved from SemMedDB. While all three types are used for the Drug→Gene1→Gene2→Cancer pathway, only the drug–gene and gene–cancer predications are used for the Drug→Gene→Cancer pathway.
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f2-cin-suppl.1-2014-103: (A) Two pathway schemas are utilized. The first connects a drug–gene predication with a gene–cancer predication and the second connects a drug–gene predication to a gene–gene predication and then the object gene of the gene–gene predication to a gene–cancer predication. (B) Drug–gene, gene–cancer, and gene–gene predications are all retrieved from SemMedDB. While all three types are used for the Drug→Gene1→Gene2→Cancer pathway, only the drug–gene and gene–cancer predications are used for the Drug→Gene→Cancer pathway.

Mentions: Step 3: Prostate cancer discovery pathways (Fig. 2)


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

Zhang R, Cairelli MJ, Fiszman M, Kilicoglu H, Rindflesch TC, Pakhomov SV, Melton GB - Cancer Inform (2014)

(A) Two pathway schemas are utilized. The first connects a drug–gene predication with a gene–cancer predication and the second connects a drug–gene predication to a gene–gene predication and then the object gene of the gene–gene predication to a gene–cancer predication. (B) Drug–gene, gene–cancer, and gene–gene predications are all retrieved from SemMedDB. While all three types are used for the Drug→Gene1→Gene2→Cancer pathway, only the drug–gene and gene–cancer predications are used for the Drug→Gene→Cancer pathway.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4216049&req=5

f2-cin-suppl.1-2014-103: (A) Two pathway schemas are utilized. The first connects a drug–gene predication with a gene–cancer predication and the second connects a drug–gene predication to a gene–gene predication and then the object gene of the gene–gene predication to a gene–cancer predication. (B) Drug–gene, gene–cancer, and gene–gene predications are all retrieved from SemMedDB. While all three types are used for the Drug→Gene1→Gene2→Cancer pathway, only the drug–gene and gene–cancer predications are used for the Drug→Gene→Cancer pathway.
Mentions: Step 3: Prostate cancer discovery pathways (Fig. 2)

Bottom Line: 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.Through both pathway schemas, we found drugs already used for prostate cancer therapy and drugs not currently listed as the prostate cancer medications.

View Article: PubMed Central - PubMed

Affiliation: Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA. ; Department of Surgery, University of Minnesota, Minneapolis, MN, USA.

ABSTRACT
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. Through 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.

No MeSH data available.


Related in: MedlinePlus