Publications by authors named "Yuka Tateisi"

4 Publications

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O-JMeSH: creating a bilingual English-Japanese controlled vocabulary of MeSH UIDs through machine translation and mutual information.

Genomics Inform 2021 Sep 30;19(3):e26. Epub 2021 Sep 30.

Graduate School of Integrative Science and Engineering, Tokyo City University, Tokyo 158-8557, Japan.

Previous approaches to create a controlled vocabulary for Japanese have resorted to existing bilingual dictionary and transformation rules to allow such mappings. However, given the possible new terms introduced due to coronavirus disease 2019 (COVID-19) and the emphasis on respiratory and infection-related terms, coverage might not be guaranteed. We propose creating a Japanese bilingual controlled vocabulary based on MeSH terms assigned to COVID-19 related publications in this work. For such, we resorted to manual curation of several bilingual dictionaries and a computational approach based on machine translation of sentences containing such terms and the ranking of possible translations for the individual terms by mutual information. Our results show that we achieved nearly 99% occurrence coverage in LitCovid, while our computational approach presented average accuracy of 63.33% for all terms, and 84.51% for drugs and chemicals.
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http://dx.doi.org/10.5808/gi.21014DOI Listing
September 2021

Constructing Japanese MeSH term dictionaries related to the COVID-19 literature.

Genomics Inform 2021 Sep 30;19(3):e25. Epub 2021 Sep 30.

Computer Science Department, The University of Sheffield, Western Bank, Sheffield S10 2TN, UK.

The coronavirus disease 2019 (COVID-19) pandemic has led to a flood of research papers and the information has been updated with considerable frequency. For society to derive benefits from this research, it is necessary to promote sharing up-to-date knowledge from these papers. However, because most research papers are written in English, it is difficult for people who are not familiar with English medical terms to obtain knowledge from them. To facilitate sharing knowledge from COVID-19 papers written in English for Japanese speakers, we tried to construct a dictionary with an open license by assigning Japanese terms to MeSH unique identifiers (UIDs) annotated to words in the texts of COVID-19 papers. Using this dictionary, 98.99% of all occurrences of MeSH terms in COVID-19 papers were covered. We also created a curated version of the dictionary and uploaded it to PubDictionary for wider use in the PubAnnotation system.
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http://dx.doi.org/10.5808/gi.21012DOI Listing
September 2021

Resources for assigning MeSH IDs to Japanese medical terms.

Authors:
Yuka Tateisi

Genomics Inform 2019 Jun 27;17(2):e16. Epub 2019 Jun 27.

National Bioscience Database Center, Japan Science and Technology Agency, Tokyo 102-8666, Japan.

Medical Subject Headings (MeSH), a medical thesaurus created by the National Library of Medicine (NLM), is a useful resource for natural language processing (NLP). In this article, the current status of the Japanese version of Medical Subject Headings (MeSH) is reviewed. Online investigation found that Japanese-English dictionaries, which assign MeSH information to applicable terms, but use them for NLP, were found to be difficult to access, due to license restrictions. Here, we investigate an open-source Japanese-English glossary as an alternative method for assigning MeSH IDs to Japanese terms, to obtain preliminary data for NLP proof-of-concept.
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http://dx.doi.org/10.5808/GI.2019.17.2.e16DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6808631PMC
June 2019

New challenges for text mining: mapping between text and manually curated pathways.

BMC Bioinformatics 2008 Apr 11;9 Suppl 3:S5. Epub 2008 Apr 11.

Department of Computer Science, Graduate School of Information Science and Technology, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan.

Background: Associating literature with pathways poses new challenges to the Text Mining (TM) community. There are three main challenges to this task: (1) the identification of the mapping position of a specific entity or reaction in a given pathway, (2) the recognition of the causal relationships among multiple reactions, and (3) the formulation and implementation of required inferences based on biological domain knowledge.

Results: To address these challenges, we constructed new resources to link the text with a model pathway; they are: the GENIA pathway corpus with event annotation and NF-kB pathway. Through their detailed analysis, we address the untapped resource, 'bio-inference,' as well as the differences between text and pathway representation. Here, we show the precise comparisons of their representations and the nine classes of 'bio-inference' schemes observed in the pathway corpus.

Conclusions: We believe that the creation of such rich resources and their detailed analysis is the significant first step for accelerating the research of the automatic construction of pathway from text.
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http://dx.doi.org/10.1186/1471-2105-9-S3-S5DOI Listing
April 2008
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