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Advances in the development of PubCaseFinder, including the new application programming interface and matching algorithm.
Fujiwara, Toyofumi; Shin, Jae-Moon; Yamaguchi, Atsuko.
Afiliação
  • Fujiwara T; Database Center for Life Science, Joint Support-Center for Data Science Research, Research Organization of Information and Systems, Kashiwa-shi, Chiba-ken, Japan.
  • Shin JM; Database Center for Life Science, Joint Support-Center for Data Science Research, Research Organization of Information and Systems, Kashiwa-shi, Chiba-ken, Japan.
  • Yamaguchi A; Graduate School of Integrative Science and Engineering, Tokyo City University, Setagaya-ku, Tokyo, Japan.
Hum Mutat ; 43(6): 734-742, 2022 06.
Article em En | MEDLINE | ID: mdl-35143083
ABSTRACT
Over 10,000 rare genetic diseases have been identified, and millions of newborns are affected by severe rare genetic diseases each year. A variety of Human Phenotype Ontology (HPO)-based clinical decision support systems (CDSS) and patient repositories have been developed to support clinicians in diagnosing patients with suspected rare genetic diseases. In September 2017, we released PubCaseFinder (https//pubcasefinder.dbcls.jp), a web-based CDSS that provides ranked lists of genetic and rare diseases using HPO-based phenotypic similarities, where top-listed diseases represent the most likely differential diagnosis. We also developed a Matchmaker Exchange (MME) application programming interface (API) to query PubCaseFinder, which has been adopted by several patient repositories. In this paper, we describe notable updates regarding PubCaseFinder, the GeneYenta matching algorithm implemented in PubCaseFinder, and the PubCaseFinder API. The updated GeneYenta matching algorithm improves the performance of the CDSS automated differential diagnosis function. Moreover, the updated PubCaseFinder and new API empower patient repositories participating in MME and medical professionals to actively use HPO-based resources.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Bases de Dados Genéticas Tipo de estudo: Prognostic_studies Limite: Humans / Newborn Idioma: En Revista: Hum Mutat Assunto da revista: GENETICA MEDICA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Japão

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Bases de Dados Genéticas Tipo de estudo: Prognostic_studies Limite: Humans / Newborn Idioma: En Revista: Hum Mutat Assunto da revista: GENETICA MEDICA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Japão