Bioregulatory event extraction using large language models: a case study of rice literature
Abstract The extraction of biological regulation events has been a key focus in the field of biomedical nature language processing (BioNLP). However, existing methods often encounter challenges such as cascading errors in text mining pipelines and limitations in topic coverage from the selected corp...
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| Format: | Article |
| Language: | English |
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BioMed Central
2024-10-01
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| Series: | Genomics & Informatics |
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| Online Access: | https://doi.org/10.1186/s44342-024-00022-3 |
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| author | Xinzhi Yao Zhihan He Jingbo Xia |
| author_facet | Xinzhi Yao Zhihan He Jingbo Xia |
| author_sort | Xinzhi Yao |
| collection | DOAJ |
| description | Abstract The extraction of biological regulation events has been a key focus in the field of biomedical nature language processing (BioNLP). However, existing methods often encounter challenges such as cascading errors in text mining pipelines and limitations in topic coverage from the selected corpus. Fortunately, the emergence of large language models (LLMs) presents a potential solution due to their robust semantic understanding and extensive knowledge base. To explore this potential, our project at the Biomedical Linked Annotation Hackathon 8 (BLAH 8) investigates the feasibility of using LLMs to extract biological regulation events. Our findings, based on the analysis of rice literature, demonstrate the promising performance of LLMs in this task, while also highlighting several concerns that must be addressed in future LLM-based application in low-resource topic. |
| format | Article |
| id | doaj-art-00806be6fd184bb8b88e3359dbd8523a |
| institution | DOAJ |
| issn | 2234-0742 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | BioMed Central |
| record_format | Article |
| series | Genomics & Informatics |
| spelling | doaj-art-00806be6fd184bb8b88e3359dbd8523a2025-08-20T03:16:51ZengBioMed CentralGenomics & Informatics2234-07422024-10-012211510.1186/s44342-024-00022-3Bioregulatory event extraction using large language models: a case study of rice literatureXinzhi Yao0Zhihan He1Jingbo Xia2College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural UniversityCollege of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural UniversityCollege of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural UniversityAbstract The extraction of biological regulation events has been a key focus in the field of biomedical nature language processing (BioNLP). However, existing methods often encounter challenges such as cascading errors in text mining pipelines and limitations in topic coverage from the selected corpus. Fortunately, the emergence of large language models (LLMs) presents a potential solution due to their robust semantic understanding and extensive knowledge base. To explore this potential, our project at the Biomedical Linked Annotation Hackathon 8 (BLAH 8) investigates the feasibility of using LLMs to extract biological regulation events. Our findings, based on the analysis of rice literature, demonstrate the promising performance of LLMs in this task, while also highlighting several concerns that must be addressed in future LLM-based application in low-resource topic.https://doi.org/10.1186/s44342-024-00022-3Oryza sativaBioregulatory eventText miningLarge language modelPrompt engineering |
| spellingShingle | Xinzhi Yao Zhihan He Jingbo Xia Bioregulatory event extraction using large language models: a case study of rice literature Genomics & Informatics Oryza sativa Bioregulatory event Text mining Large language model Prompt engineering |
| title | Bioregulatory event extraction using large language models: a case study of rice literature |
| title_full | Bioregulatory event extraction using large language models: a case study of rice literature |
| title_fullStr | Bioregulatory event extraction using large language models: a case study of rice literature |
| title_full_unstemmed | Bioregulatory event extraction using large language models: a case study of rice literature |
| title_short | Bioregulatory event extraction using large language models: a case study of rice literature |
| title_sort | bioregulatory event extraction using large language models a case study of rice literature |
| topic | Oryza sativa Bioregulatory event Text mining Large language model Prompt engineering |
| url | https://doi.org/10.1186/s44342-024-00022-3 |
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