Predicting drug characteristics using biomedical text embedding
Abstract Background Drug–drug interactions (DDIs) are preventable causes of medical injuries and often result in doctor and emergency room visits. Previous research demonstrates the effectiveness of using matrix completion approaches based on known drug interactions to predict unknown Drug–drug inte...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-12-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-022-05083-1 |
| _version_ | 1811178980423761920 |
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| author | Guy Shtar Asnat Greenstein-Messica Eyal Mazuz Lior Rokach Bracha Shapira |
| author_facet | Guy Shtar Asnat Greenstein-Messica Eyal Mazuz Lior Rokach Bracha Shapira |
| author_sort | Guy Shtar |
| collection | DOAJ |
| description | Abstract Background Drug–drug interactions (DDIs) are preventable causes of medical injuries and often result in doctor and emergency room visits. Previous research demonstrates the effectiveness of using matrix completion approaches based on known drug interactions to predict unknown Drug–drug interactions. However, in the case of a new drug, where there is limited or no knowledge regarding the drug’s existing interactions, such an approach is unsuitable, and other drug’s preferences can be used to accurately predict new Drug–drug interactions. Methods We propose adjacency biomedical text embedding (ABTE) to address this limitation by using a hybrid approach which combines known drugs’ interactions and the drug’s biomedical text embeddings to predict the DDIs of both new and well known drugs. Results Our evaluation demonstrates the superiority of this approach compared to recently published DDI prediction models and matrix factorization-based approaches. Furthermore, we compared the use of different text embedding methods in ABTE, and found that the concept embedding approach, which involves biomedical information in the embedding process, provides the highest performance for this task. Additionally, we demonstrate the effectiveness of leveraging biomedical text embedding for additional drugs’ biomedical prediction task by presenting text embedding’s contribution to a multi-modal pregnancy drug safety classification. Conclusion Text and concept embeddings created by analyzing a domain-specific large-scale biomedical corpora can be used for predicting drug-related properties such as Drug–drug interactions and drug safety prediction. Prediction models based on the embeddings resulted in comparable results to hand-crafted features, however text embeddings do not require manual categorization or data collection and rely solely on the published literature. |
| first_indexed | 2024-04-11T06:27:08Z |
| format | Article |
| id | doaj.art-3e7e4a6b73e54f5aaee39593177b18e7 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-11T06:27:08Z |
| publishDate | 2022-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-3e7e4a6b73e54f5aaee39593177b18e72022-12-22T04:40:18ZengBMCBMC Bioinformatics1471-21052022-12-0123111710.1186/s12859-022-05083-1Predicting drug characteristics using biomedical text embeddingGuy Shtar0Asnat Greenstein-Messica1Eyal Mazuz2Lior Rokach3Bracha Shapira4Department of Software and Information Systems Engineering, Ben-Gurion University of the NegevDepartment of Software and Information Systems Engineering, Ben-Gurion University of the NegevDepartment of Software and Information Systems Engineering, Ben-Gurion University of the NegevDepartment of Software and Information Systems Engineering, Ben-Gurion University of the NegevDepartment of Software and Information Systems Engineering, Ben-Gurion University of the NegevAbstract Background Drug–drug interactions (DDIs) are preventable causes of medical injuries and often result in doctor and emergency room visits. Previous research demonstrates the effectiveness of using matrix completion approaches based on known drug interactions to predict unknown Drug–drug interactions. However, in the case of a new drug, where there is limited or no knowledge regarding the drug’s existing interactions, such an approach is unsuitable, and other drug’s preferences can be used to accurately predict new Drug–drug interactions. Methods We propose adjacency biomedical text embedding (ABTE) to address this limitation by using a hybrid approach which combines known drugs’ interactions and the drug’s biomedical text embeddings to predict the DDIs of both new and well known drugs. Results Our evaluation demonstrates the superiority of this approach compared to recently published DDI prediction models and matrix factorization-based approaches. Furthermore, we compared the use of different text embedding methods in ABTE, and found that the concept embedding approach, which involves biomedical information in the embedding process, provides the highest performance for this task. Additionally, we demonstrate the effectiveness of leveraging biomedical text embedding for additional drugs’ biomedical prediction task by presenting text embedding’s contribution to a multi-modal pregnancy drug safety classification. Conclusion Text and concept embeddings created by analyzing a domain-specific large-scale biomedical corpora can be used for predicting drug-related properties such as Drug–drug interactions and drug safety prediction. Prediction models based on the embeddings resulted in comparable results to hand-crafted features, however text embeddings do not require manual categorization or data collection and rely solely on the published literature.https://doi.org/10.1186/s12859-022-05083-1Drug interactionsText miningMachine learning |
| spellingShingle | Guy Shtar Asnat Greenstein-Messica Eyal Mazuz Lior Rokach Bracha Shapira Predicting drug characteristics using biomedical text embedding BMC Bioinformatics Drug interactions Text mining Machine learning |
| title | Predicting drug characteristics using biomedical text embedding |
| title_full | Predicting drug characteristics using biomedical text embedding |
| title_fullStr | Predicting drug characteristics using biomedical text embedding |
| title_full_unstemmed | Predicting drug characteristics using biomedical text embedding |
| title_short | Predicting drug characteristics using biomedical text embedding |
| title_sort | predicting drug characteristics using biomedical text embedding |
| topic | Drug interactions Text mining Machine learning |
| url | https://doi.org/10.1186/s12859-022-05083-1 |
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