OryzaGP: rice gene and protein dataset for named-entity recognition

Abstract

2019, Korea Genome Organization This is an open-access article distributed under the terms of the Creative Commons Attribution license (http:// creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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.

Editor’s introduction to the special section on the 7th Biomedical Linked Annotation Hackathon (BLAH7)

“…They developed two versions of Japanese translation of MeSH terms, one through merging of existing resources and manual curation, and another through an automatic translation method, of which the results are reported in the two separate application notes. Larmande et al [11] reports a revision to OryzaGP [12], a corpus of PubMed articles relevant to rice species, which are automatically annotated for proteins and genes. The last one by Dohi et al [13] presents the authors' opinion after their case study with Alexander disease towards visualizing the phenotype diversity.…”