Deep Denoising of Raw Biomedical Knowledge Graph From COVID-19 Literature, LitCovid, and Pubtator: Framework Development and Validation.

Jiang, Chao; Ngo, Victoria; Chapman, Richard; Yu, Yue; Liu, Hongfang; Jiang, Guoqian; Zong, Nansu

Jiang, Chao; Ngo, Victoria; Chapman, Richard; Yu, Yue; Liu, Hongfang; Jiang, Guoqian; Zong, Nansu.

Afiliação

Jiang C; Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, United States.
Ngo V; Center for Innovation to Implementation, VA Palo Alto Health Care System, Sacramento, CA, United States.
Chapman R; Stanford Health Policy, Stanford School of Medicine, Stanford University, Stanford, CA, United States.
Yu Y; Freeman Spogli Institute for International Studies, Stanford University, Stanford, CA, United States.
Liu H; Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, United States.
Jiang G; Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, United States.
Zong N; Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, United States.

J Med Internet Res ; 24(7): e38584, 2022 07 06.

Article em En | MEDLINE | ID: mdl-35658098

Assuntos

COVID-19; Humanos; Conhecimento; Redes Neurais de Computação; Reconhecimento Automatizado de Padrão; Curva ROC

Palavras-chave

COVID-19; adversarial generative network; biomedical; deep denoising; knowledge graph; machine learning; network model; neural network; training data

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: COVID-19 Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google