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T4SEfinder: a bioinformatics tool for genome-scale prediction of bacterial type IV secreted effectors using pre-trained protein language model.
Zhang, Yumeng; Zhang, Yangming; Xiong, Yi; Wang, Hui; Deng, Zixin; Song, Jiangning; Ou, Hong-Yu.
Afiliação
  • Zhang Y; State Key Laboratory of Microbial Metabolism, Joint International Laboratory on Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, China.
  • Zhang Y; State Key Laboratory of Microbial Metabolism, Joint International Laboratory on Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, China.
  • Xiong Y; State Key Laboratory of Microbial Metabolism, Joint International Laboratory on Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, China.
  • Wang H; State Key Laboratory of Pathogens and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China.
  • Deng Z; State Key Laboratory of Microbial Metabolism, Joint International Laboratory on Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, China.
  • Song J; Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia.
  • Ou HY; State Key Laboratory of Microbial Metabolism, Joint International Laboratory on Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, China.
Brief Bioinform ; 23(1)2022 01 17.
Article em En | MEDLINE | ID: mdl-34657153
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Biologia Computacional / Idioma Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Biologia Computacional / Idioma Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article