A Statistical Test for Differential Network Analysis Based on Inference of Gaussian Graphical Model.

He, Hao; Cao, Shaolong; Zhang, Ji-Gang; Shen, Hui; Wang, Yu-Ping; Deng, Hong-Wen

He, Hao; Cao, Shaolong; Zhang, Ji-Gang; Shen, Hui; Wang, Yu-Ping; Deng, Hong-Wen.

Afiliação

He H; Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, 70112, USA.
Cao S; Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, 70112, USA.
Zhang JG; Department of Biomedical Engineering, Tulane University, New Orleans, LA, 70118, USA.
Shen H; Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, 70112, USA.
Wang YP; Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, 70112, USA.
Deng HW; Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, 70112, USA.

Sci Rep ; 9(1): 10863, 2019 07 26.

Article em En | MEDLINE | ID: mdl-31350445

Assuntos

Adenocarcinoma de Pulmão/genética; Biologia Computacional/métodos; Regulação Neoplásica da Expressão Gênica; Redes Reguladoras de Genes; Neoplasias Pulmonares/genética; Modelos Estatísticos; Algoritmos; Correlação de Dados; Confiabilidade dos Dados; Humanos; Distribuição Normal; RNA-Seq

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Regulação Neoplásica da Expressão Gênica / Modelos Estatísticos / Biologia Computacional / Redes Reguladoras de Genes / Adenocarcinoma de Pulmão / Neoplasias Pulmonares Tipo de estudo: Risk_factors_studies Limite: Humans Idioma: En Revista: Sci Rep Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Estados Unidos

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