Your browser doesn't support javascript.
loading
DDBJ Data Analysis Challenge: a machine learning competition to predict Arabidopsis chromatin feature annotations from DNA sequences.
Kaminuma, Eli; Baba, Yukino; Mochizuki, Masahiro; Matsumoto, Hirotaka; Ozaki, Haruka; Okayama, Toshitsugu; Kato, Takuya; Oki, Shinya; Fujisawa, Takatomo; Nakamura, Yasukazu; Arita, Masanori; Ogasawara, Osamu; Kashima, Hisashi; Takagi, Toshihisa.
Afiliação
  • Kaminuma E; Center for Information Biology, National Institute of Genetics.
  • Baba Y; Graduate School of Informatics, Kyoto University.
  • Mochizuki M; IMSBIO Co., Ltd.
  • Matsumoto H; Laboratory for Bioinformatics Research, RIKEN Center for Biosystems Dynamics Research.
  • Ozaki H; Laboratory for Bioinformatics Research, RIKEN Center for Biosystems Dynamics Research.
  • Okayama T; BITS Co., Ltd.
  • Kato T; Graduate School of Information Science and Technology, The University of Tokyo.
  • Oki S; Graduate School of Medical Sciences, Kyushu University.
  • Fujisawa T; Center for Information Biology, National Institute of Genetics.
  • Nakamura Y; Center for Information Biology, National Institute of Genetics.
  • Arita M; Center for Information Biology, National Institute of Genetics.
  • Ogasawara O; Center for Information Biology, National Institute of Genetics.
  • Kashima H; Graduate School of Informatics, Kyoto University.
  • Takagi T; Center for Information Biology, National Institute of Genetics.
Genes Genet Syst ; 95(1): 43-50, 2020 Apr 22.
Article em En | MEDLINE | ID: mdl-32213716
Assuntos
Palavras-chave
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Cromatina / Arabidopsis / Genoma de Planta / Bases de Dados de Ácidos Nucleicos / Aprendizado de Máquina Tipo de estudo: Prognostic_studies / Risk_factors_studies País/Região como assunto: Asia Idioma: En Revista: Genes Genet Syst Assunto da revista: BIOLOGIA MOLECULAR / BIOTECNOLOGIA Ano de publicação: 2020 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: Cromatina / Arabidopsis / Genoma de Planta / Bases de Dados de Ácidos Nucleicos / Aprendizado de Máquina Tipo de estudo: Prognostic_studies / Risk_factors_studies País/Região como assunto: Asia Idioma: En Revista: Genes Genet Syst Assunto da revista: BIOLOGIA MOLECULAR / BIOTECNOLOGIA Ano de publicação: 2020 Tipo de documento: Article