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Adverse Drug Reaction Discovery Using a Tumor-Biomarker Knowledge Graph
Adverse drug reactions (ADRs) are a major public health concern, and early detection is crucial for drug development and patient safety. Together with the increasing availability of large-scale literature data, machine learning has the potential to predict unknown ADRs from current knowledge. By the...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7873847/ https://www.ncbi.nlm.nih.gov/pubmed/33584816 http://dx.doi.org/10.3389/fgene.2020.625659 |
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author | Wang, Meng Ma, Xinyu Si, Jingwen Tang, Hongjia Wang, Haofen Li, Tunliang Ouyang, Wen Gong, Liying Tang, Yongzhong He, Xi Huang, Wei Liu, Xing |
author_facet | Wang, Meng Ma, Xinyu Si, Jingwen Tang, Hongjia Wang, Haofen Li, Tunliang Ouyang, Wen Gong, Liying Tang, Yongzhong He, Xi Huang, Wei Liu, Xing |
author_sort | Wang, Meng |
collection | PubMed |
description | Adverse drug reactions (ADRs) are a major public health concern, and early detection is crucial for drug development and patient safety. Together with the increasing availability of large-scale literature data, machine learning has the potential to predict unknown ADRs from current knowledge. By the machine learning methods, we constructed a Tumor-Biomarker Knowledge Graph (TBKG) which contains four types of node: Tumor, Biomarker, Drug, and ADR using biomedical literatures. Based on this knowledge graph, we not only discovered potential ADRs of antitumor drugs but also provided explanations. Experiments on real-world data show that our model can achieve 0.81 accuracy of three cross-validation and the ADRs discovery of Osimertinib was chosen for the clinical validation. Calculated ADRs of Osimertinib by our model consisted of the known ADRs which were in line with the official manual and some unreported rare ADRs in clinical cases. Results also showed that our model outperformed traditional co-occurrence methods. Moreover, each calculated ADRs were attached with the corresponding paths of “tumor-biomarker-drug” in the knowledge graph which could help to obtain in-depth insights into the underlying mechanisms. In conclusion, the tumor-biomarker knowledge-graph based approach is an explainable method for potential ADRs discovery based on biomarkers and might be valuable to the community working on the emerging field of biomedical literature mining and provide impetus for the mechanism research of ADRs. |
format | Online Article Text |
id | pubmed-7873847 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78738472021-02-11 Adverse Drug Reaction Discovery Using a Tumor-Biomarker Knowledge Graph Wang, Meng Ma, Xinyu Si, Jingwen Tang, Hongjia Wang, Haofen Li, Tunliang Ouyang, Wen Gong, Liying Tang, Yongzhong He, Xi Huang, Wei Liu, Xing Front Genet Genetics Adverse drug reactions (ADRs) are a major public health concern, and early detection is crucial for drug development and patient safety. Together with the increasing availability of large-scale literature data, machine learning has the potential to predict unknown ADRs from current knowledge. By the machine learning methods, we constructed a Tumor-Biomarker Knowledge Graph (TBKG) which contains four types of node: Tumor, Biomarker, Drug, and ADR using biomedical literatures. Based on this knowledge graph, we not only discovered potential ADRs of antitumor drugs but also provided explanations. Experiments on real-world data show that our model can achieve 0.81 accuracy of three cross-validation and the ADRs discovery of Osimertinib was chosen for the clinical validation. Calculated ADRs of Osimertinib by our model consisted of the known ADRs which were in line with the official manual and some unreported rare ADRs in clinical cases. Results also showed that our model outperformed traditional co-occurrence methods. Moreover, each calculated ADRs were attached with the corresponding paths of “tumor-biomarker-drug” in the knowledge graph which could help to obtain in-depth insights into the underlying mechanisms. In conclusion, the tumor-biomarker knowledge-graph based approach is an explainable method for potential ADRs discovery based on biomarkers and might be valuable to the community working on the emerging field of biomedical literature mining and provide impetus for the mechanism research of ADRs. Frontiers Media S.A. 2021-01-12 /pmc/articles/PMC7873847/ /pubmed/33584816 http://dx.doi.org/10.3389/fgene.2020.625659 Text en Copyright © 2021 Wang, Ma, Si, Tang, Wang, Li, Ouyang, Gong, Tang, He, Huang and Liu. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Wang, Meng Ma, Xinyu Si, Jingwen Tang, Hongjia Wang, Haofen Li, Tunliang Ouyang, Wen Gong, Liying Tang, Yongzhong He, Xi Huang, Wei Liu, Xing Adverse Drug Reaction Discovery Using a Tumor-Biomarker Knowledge Graph |
title | Adverse Drug Reaction Discovery Using a Tumor-Biomarker Knowledge Graph |
title_full | Adverse Drug Reaction Discovery Using a Tumor-Biomarker Knowledge Graph |
title_fullStr | Adverse Drug Reaction Discovery Using a Tumor-Biomarker Knowledge Graph |
title_full_unstemmed | Adverse Drug Reaction Discovery Using a Tumor-Biomarker Knowledge Graph |
title_short | Adverse Drug Reaction Discovery Using a Tumor-Biomarker Knowledge Graph |
title_sort | adverse drug reaction discovery using a tumor-biomarker knowledge graph |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7873847/ https://www.ncbi.nlm.nih.gov/pubmed/33584816 http://dx.doi.org/10.3389/fgene.2020.625659 |
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