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GNorm2: an improved gene name recognition and normalization system.
Wei, Chih-Hsuan; Luo, Ling; Islamaj, Rezarta; Lai, Po-Ting; Lu, Zhiyong.
Afiliação
  • Wei CH; National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, United States.
  • Luo L; School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.
  • Islamaj R; National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, United States.
  • Lai PT; National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, United States.
  • Lu Z; National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, United States.
Bioinformatics ; 39(10)2023 10 03.
Article em En | MEDLINE | ID: mdl-37878810
ABSTRACT
MOTIVATION Gene name normalization is an important yet highly complex task in biomedical text mining research, as gene names can be highly ambiguous and may refer to different genes in different species or share similar names with other bioconcepts. This poses a challenge for accurately identifying and linking gene mentions to their corresponding entries in databases such as NCBI Gene or UniProt. While there has been a body of literature on the gene normalization task, few have addressed all of these challenges or make their solutions publicly available to the scientific community.

RESULTS:

Building on the success of GNormPlus, we have created GNorm2 a more advanced tool with optimized functions and improved performance. GNorm2 integrates a range of advanced deep learning-based methods, resulting in the highest levels of accuracy and efficiency for gene recognition and normalization to date. Our tool is freely available for download. AVAILABILITY AND IMPLEMENTATION https//github.com/ncbi/GNorm2.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Mineração de Dados Idioma: En Revista: Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Mineração de Dados Idioma: En Revista: Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos