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Updated: Apr 21, 2026

Phosphopeptide Enrichment Coupled with Label-free Quantitative Mass Spectrometry to Investigate the Phosphoproteome in Prostate Cancer
Published on: August 2, 2018
Exploiting Literature-derived Knowledge and Semantics to Identify Potential Prostate Cancer Drugs
Rui Zhang1, Michael J Cairelli2, Marcelo Fiszman2
1Institute 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.
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