Extract antibody and antigen names from biomedical literature
Abstract Background The roles of antibody and antigen are indispensable in targeted diagnosis, therapy, and biomedical discovery. On top of that, massive numbers of new scientific articles about antibodies and/or antigens are published each year, which is a precious knowledge resource but has yet be...
| 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/s12859-022-04993-4 |
| _version_ | 1811204001633402880 |
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| author | Thuy Trang Dinh Trang Phuong Vo-Chanh Chau Nguyen Viet Quoc Huynh Nam Vo Hoang Duc Nguyen |
| author_facet | Thuy Trang Dinh Trang Phuong Vo-Chanh Chau Nguyen Viet Quoc Huynh Nam Vo Hoang Duc Nguyen |
| author_sort | Thuy Trang Dinh |
| collection | DOAJ |
| description | Abstract Background The roles of antibody and antigen are indispensable in targeted diagnosis, therapy, and biomedical discovery. On top of that, massive numbers of new scientific articles about antibodies and/or antigens are published each year, which is a precious knowledge resource but has yet been exploited to its full potential. We, therefore, aim to develop a biomedical natural language processing tool that can automatically identify antibody and antigen entities from articles. Results We first annotated an antibody-antigen corpus including 3210 relevant PubMed abstracts using a semi-automatic approach. The Inter-Annotator Agreement score of 3 annotators ranges from 91.46 to 94.31%, indicating that the annotations are consistent and the corpus is reliable. We then used the corpus to develop and optimize BiLSTM-CRF-based and BioBERT-based models. The models achieved overall F1 scores of 62.49% and 81.44%, respectively, which showed potential for newly studied entities. The two models served as foundation for development of a named entity recognition (NER) tool that automatically recognizes antibody and antigen names from biomedical literature. Conclusions Our antibody-antigen NER models enable users to automatically extract antibody and antigen names from scientific articles without manually scanning through vast amounts of data and information in the literature. The output of NER can be used to automatically populate antibody-antigen databases, support antibody validation, and facilitate researchers with the most appropriate antibodies of interest. The packaged NER model is available at https://github.com/TrangDinh44/ABAG_BioBERT.git . |
| first_indexed | 2024-04-12T03:04:25Z |
| format | Article |
| id | doaj.art-da2e5a068cb747b4bb37b598f94ee0fd |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-12T03:04:25Z |
| publishDate | 2022-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-da2e5a068cb747b4bb37b598f94ee0fd2022-12-22T03:50:33ZengBMCBMC Bioinformatics1471-21052022-12-0123112110.1186/s12859-022-04993-4Extract antibody and antigen names from biomedical literatureThuy Trang Dinh0Trang Phuong Vo-Chanh1Chau Nguyen2Viet Quoc Huynh3Nam Vo4Hoang Duc Nguyen5Center for Bioscience and Biotechnology, University of ScienceCenter for Bioscience and Biotechnology, University of ScienceCenter for Bioscience and Biotechnology, University of ScienceCenter for Bioscience and Biotechnology, University of ScienceCenter for Bioscience and Biotechnology, University of ScienceCenter for Bioscience and Biotechnology, University of ScienceAbstract Background The roles of antibody and antigen are indispensable in targeted diagnosis, therapy, and biomedical discovery. On top of that, massive numbers of new scientific articles about antibodies and/or antigens are published each year, which is a precious knowledge resource but has yet been exploited to its full potential. We, therefore, aim to develop a biomedical natural language processing tool that can automatically identify antibody and antigen entities from articles. Results We first annotated an antibody-antigen corpus including 3210 relevant PubMed abstracts using a semi-automatic approach. The Inter-Annotator Agreement score of 3 annotators ranges from 91.46 to 94.31%, indicating that the annotations are consistent and the corpus is reliable. We then used the corpus to develop and optimize BiLSTM-CRF-based and BioBERT-based models. The models achieved overall F1 scores of 62.49% and 81.44%, respectively, which showed potential for newly studied entities. The two models served as foundation for development of a named entity recognition (NER) tool that automatically recognizes antibody and antigen names from biomedical literature. Conclusions Our antibody-antigen NER models enable users to automatically extract antibody and antigen names from scientific articles without manually scanning through vast amounts of data and information in the literature. The output of NER can be used to automatically populate antibody-antigen databases, support antibody validation, and facilitate researchers with the most appropriate antibodies of interest. The packaged NER model is available at https://github.com/TrangDinh44/ABAG_BioBERT.git .https://doi.org/10.1186/s12859-022-04993-4AntibodyAntigenCorpusNamed entity recognitionBioNLPSemi-automatic annotation |
| spellingShingle | Thuy Trang Dinh Trang Phuong Vo-Chanh Chau Nguyen Viet Quoc Huynh Nam Vo Hoang Duc Nguyen Extract antibody and antigen names from biomedical literature BMC Bioinformatics Antibody Antigen Corpus Named entity recognition BioNLP Semi-automatic annotation |
| title | Extract antibody and antigen names from biomedical literature |
| title_full | Extract antibody and antigen names from biomedical literature |
| title_fullStr | Extract antibody and antigen names from biomedical literature |
| title_full_unstemmed | Extract antibody and antigen names from biomedical literature |
| title_short | Extract antibody and antigen names from biomedical literature |
| title_sort | extract antibody and antigen names from biomedical literature |
| topic | Antibody Antigen Corpus Named entity recognition BioNLP Semi-automatic annotation |
| url | https://doi.org/10.1186/s12859-022-04993-4 |
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