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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2014-01-01
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| Series: | Cancer Informatics |
| Online Access: | https://doi.org/10.4137/CIN.S13889 |
| _version_ | 1818306954220535808 |
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| author | Rui Zhang Michael J. Cairelli Marcelo Fiszman Halil Kilicoglu Thomas C. Rindflesch Serguei V. Pakhomov Genevieve B. Melton |
| author_facet | Rui Zhang Michael J. Cairelli Marcelo Fiszman Halil Kilicoglu Thomas C. Rindflesch Serguei V. Pakhomov Genevieve B. Melton |
| author_sort | Rui Zhang |
| collection | DOAJ |
| 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. 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. |
| first_indexed | 2024-12-13T06:50:41Z |
| format | Article |
| id | doaj.art-ea655865218843488716ac5ff64cc205 |
| institution | Directory Open Access Journal |
| issn | 1176-9351 |
| language | English |
| last_indexed | 2024-12-13T06:50:41Z |
| publishDate | 2014-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Cancer Informatics |
| spelling | doaj.art-ea655865218843488716ac5ff64cc2052022-12-21T23:56:10ZengSAGE PublishingCancer Informatics1176-93512014-01-0113s110.4137/CIN.S13889Exploiting Literature-derived Knowledge and Semantics to Identify Potential Prostate Cancer DrugsRui Zhang0Michael J. Cairelli1Marcelo Fiszman2Halil Kilicoglu3Thomas C. Rindflesch4Serguei V. Pakhomov5Genevieve B. Melton6Department of Surgery, University of Minnesota, Minneapolis, MN, USA.Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.College of Pharmacy, University of Minnesota, Minneapolis, MN, USA.Department of Surgery, University of Minnesota, Minneapolis, MN, USA.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.https://doi.org/10.4137/CIN.S13889 |
| spellingShingle | Rui Zhang Michael J. Cairelli Marcelo Fiszman Halil Kilicoglu Thomas C. Rindflesch Serguei V. Pakhomov Genevieve B. Melton Exploiting Literature-derived Knowledge and Semantics to Identify Potential Prostate Cancer Drugs Cancer Informatics |
| title | 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_short | 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 |
| url | https://doi.org/10.4137/CIN.S13889 |
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