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 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.
Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA. ; Department of Surgery, University of Minnesota, Minneapolis, MN, USA.
Rui
R
Zhang
Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
Michael J
MJ
Cairelli
Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
Marcelo
M
Fiszman
Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
Halil
H
Kilicoglu
Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
Thomas C
TC
Rindflesch
Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA. ; College of Pharmacy, University of Minnesota, Minneapolis, MN, USA.
Serguei V
SV
Pakhomov
Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA. ; Department of Surgery, University of Minnesota, Minneapolis, MN, USA.
Genevieve B
GB
Melton
MEDLINE; SemMedDB; SemRep; drug discovery; natural language processing; prostate cancer; semantic predication