OryzaGP: rice gene and protein dataset for named-entity recognition
Text mining has become an important research method in biology, with its original purpose to extract biological entities, such as genes, proteins and phenotypic traits, to extend knowledge from scientific papers. However, few thorough studies on text mining and application development, for plant mol...
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| Language: | English |
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BioMed Central
2019-06-01
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| Series: | Genomics & Informatics |
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| Online Access: | http://genominfo.org/upload/pdf/gi-2019-17-2-e17.pdf |
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| author | Pierre Larmande Huy Do Yue Wang |
| author_facet | Pierre Larmande Huy Do Yue Wang |
| author_sort | Pierre Larmande |
| collection | DOAJ |
| description | Text mining has become an important research method in biology, with its original purpose to extract biological entities, such as genes, proteins and phenotypic traits, to extend knowledge from scientific papers. However, few thorough studies on text mining and application development, for plant molecular biology data, have been performed, especially for rice, resulting in a lack of datasets available to solve named-entity recognition tasks for this species. Since there are rare benchmarks available for rice, we faced various difficulties in exploiting advanced machine learning methods for accurate analysis of the rice literature. To evaluate several approaches to automatically extract information from gene/protein entities, we built a new dataset for rice as a benchmark. This dataset is composed of a set of titles and abstracts, extracted from scientific papers focusing on the rice species, and is downloaded from PubMed. During the 5th Biomedical Linked Annotation Hackathon, a portion of the dataset was uploaded to PubAnnotation for sharing. Our ultimate goal is to offer a shared task of rice gene/protein name recognition through the BioNLP Open Shared Tasks framework using the dataset, to facilitate an open comparison and evaluation of different approaches to the task. |
| format | Article |
| id | doaj-art-6e73defb9b5c4efa9f4d304f5edad4f1 |
| institution | Kabale University |
| issn | 2234-0742 |
| language | English |
| publishDate | 2019-06-01 |
| publisher | BioMed Central |
| record_format | Article |
| series | Genomics & Informatics |
| spelling | doaj-art-6e73defb9b5c4efa9f4d304f5edad4f12025-02-02T22:28:50ZengBioMed CentralGenomics & Informatics2234-07422019-06-0117210.5808/GI.2019.17.2.e17559OryzaGP: rice gene and protein dataset for named-entity recognitionPierre Larmande0Huy Do1Yue Wang2 UMR DIADE, Institute of Research for Sustainable Development (IRD), F-34394 Montpellier, France ICT Lab, University of Science and Technology of Hanoi (USTH), 100000 Hanoi, Vietnam Database Center for Life Science (DBCLS), Chiba 277-0871, JapanText mining has become an important research method in biology, with its original purpose to extract biological entities, such as genes, proteins and phenotypic traits, to extend knowledge from scientific papers. However, few thorough studies on text mining and application development, for plant molecular biology data, have been performed, especially for rice, resulting in a lack of datasets available to solve named-entity recognition tasks for this species. Since there are rare benchmarks available for rice, we faced various difficulties in exploiting advanced machine learning methods for accurate analysis of the rice literature. To evaluate several approaches to automatically extract information from gene/protein entities, we built a new dataset for rice as a benchmark. This dataset is composed of a set of titles and abstracts, extracted from scientific papers focusing on the rice species, and is downloaded from PubMed. During the 5th Biomedical Linked Annotation Hackathon, a portion of the dataset was uploaded to PubAnnotation for sharing. Our ultimate goal is to offer a shared task of rice gene/protein name recognition through the BioNLP Open Shared Tasks framework using the dataset, to facilitate an open comparison and evaluation of different approaches to the task.http://genominfo.org/upload/pdf/gi-2019-17-2-e17.pdfnamed-entity recognitionnatural language processingOryza sativaplant molecular biologyricetext mining |
| spellingShingle | Pierre Larmande Huy Do Yue Wang OryzaGP: rice gene and protein dataset for named-entity recognition Genomics & Informatics named-entity recognition natural language processing Oryza sativa plant molecular biology rice text mining |
| title | OryzaGP: rice gene and protein dataset for named-entity recognition |
| title_full | OryzaGP: rice gene and protein dataset for named-entity recognition |
| title_fullStr | OryzaGP: rice gene and protein dataset for named-entity recognition |
| title_full_unstemmed | OryzaGP: rice gene and protein dataset for named-entity recognition |
| title_short | OryzaGP: rice gene and protein dataset for named-entity recognition |
| title_sort | oryzagp rice gene and protein dataset for named entity recognition |
| topic | named-entity recognition natural language processing Oryza sativa plant molecular biology rice text mining |
| url | http://genominfo.org/upload/pdf/gi-2019-17-2-e17.pdf |
| work_keys_str_mv | AT pierrelarmande oryzagpricegeneandproteindatasetfornamedentityrecognition AT huydo oryzagpricegeneandproteindatasetfornamedentityrecognition AT yuewang oryzagpricegeneandproteindatasetfornamedentityrecognition |