Your browser doesn't support javascript.
loading
MOST: most-similar ligand based approach to target prediction.
Huang, Tao; Mi, Hong; Lin, Cheng-Yuan; Zhao, Ling; Zhong, Linda L D; Liu, Feng-Bin; Zhang, Ge; Lu, Ai-Ping; Bian, Zhao-Xiang.
Afiliação
  • Huang T; Lab of Brain and Gut Research, School of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Hong Kong, People's Republic of China.
  • Mi H; Lab of Brain and Gut Research, School of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Hong Kong, People's Republic of China.
  • Lin CY; Department of Gastroenterology, the First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510405, People's Republic of China.
  • Zhao L; Lab of Brain and Gut Research, School of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Hong Kong, People's Republic of China.
  • Zhong LL; YMU-HKBU Joint Laboratory of Traditional Natural Medicine, Yunnan Minzu University, Kunming, 650500, People's Republic of China.
  • Liu FB; Lab of Brain and Gut Research, School of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Hong Kong, People's Republic of China.
  • Zhang G; Lab of Brain and Gut Research, School of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Hong Kong, People's Republic of China.
  • Lu AP; Hong Kong Chinese Medicine Clinical Study Centre, Hong Kong Baptist University, 7 Baptist University Road, Hong Kong, People's Republic of China.
  • Bian ZX; Department of Gastroenterology, the First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510405, People's Republic of China.
BMC Bioinformatics ; 18(1): 165, 2017 Mar 11.
Article em En | MEDLINE | ID: mdl-28284192
Assuntos
Palavras-chave
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado de Máquina / Ligantes Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Animals / Humans Idioma: En Revista: BMC Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2017 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: Aprendizado de Máquina / Ligantes Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Animals / Humans Idioma: En Revista: BMC Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2017 Tipo de documento: Article