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

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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Main Authors: Zhang, Rui, Cairelli, Michael J, Fiszman, Marcelo, Kilicoglu, Halil, Rindflesch, Thomas C, Pakhomov, Serguei V, Melton, Genevieve B
Format: Online
Language:English
Published: Libertas Academica 2014
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4216049/
id pubmed-4216049
recordtype oai_dc
spelling pubmed-42160492014-11-12 Exploiting Literature-derived Knowledge and Semantics to Identify Potential Prostate Cancer Drugs Zhang, Rui Cairelli, Michael J Fiszman, Marcelo Kilicoglu, Halil Rindflesch, Thomas C Pakhomov, Serguei V Melton, Genevieve B Original Research 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. Libertas Academica 2014-10-14 /pmc/articles/PMC4216049/ /pubmed/25392688 http://dx.doi.org/10.4137/CIN.S13889 Text en © 2014 the author(s), publisher and licensee Libertas Academica Ltd. This is an open-access article distributed under the terms of the Creative Commons CC-BY-NC 3.0 License.
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Zhang, Rui
Cairelli, Michael J
Fiszman, Marcelo
Kilicoglu, Halil
Rindflesch, Thomas C
Pakhomov, Serguei V
Melton, Genevieve B
spellingShingle Zhang, Rui
Cairelli, Michael J
Fiszman, Marcelo
Kilicoglu, Halil
Rindflesch, Thomas C
Pakhomov, Serguei V
Melton, Genevieve B
Exploiting Literature-derived Knowledge and Semantics to Identify Potential Prostate Cancer Drugs
author_facet Zhang, Rui
Cairelli, Michael J
Fiszman, Marcelo
Kilicoglu, Halil
Rindflesch, Thomas C
Pakhomov, Serguei V
Melton, Genevieve B
author_sort Zhang, Rui
title Exploiting Literature-derived Knowledge and Semantics to Identify Potential Prostate Cancer Drugs
title_short Exploiting Literature-derived Knowledge and Semantics to Identify Potential Prostate Cancer Drugs
title_full Exploiting Literature-derived Knowledge and Semantics to Identify Potential Prostate Cancer Drugs
title_fullStr Exploiting Literature-derived Knowledge and Semantics to Identify Potential Prostate Cancer Drugs
title_full_unstemmed Exploiting Literature-derived Knowledge and Semantics to Identify Potential Prostate Cancer Drugs
title_sort exploiting literature-derived knowledge and semantics to identify potential prostate cancer drugs
description 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.
publisher Libertas Academica
publishDate 2014
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4216049/
_version_ 1613151043070197760

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