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WEVar: a novel statistical learning framework for predicting noncoding regulatory variants.
Wang, Ye; Jiang, Yuchao; Yao, Bing; Huang, Kun; Liu, Yunlong; Wang, Yue; Qin, Xiao; Saykin, Andrew J; Chen, Li.
Afiliación
  • Wang Y; Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
  • Jiang Y; Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
  • Yao B; Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, 36849, USA.
  • Huang K; Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA.
  • Liu Y; Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
  • Wang Y; Department of Human Genetics, Emory University, Atlanta, GA 30322, USA.
  • Qin X; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
  • Saykin AJ; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
  • Chen L; Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
Brief Bioinform ; 22(6)2021 11 05.
Article en En | MEDLINE | ID: mdl-34021560
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Variación Genética / Programas Informáticos / Modelos Estadísticos / Biología Computacional / ARN no Traducido / Secuencias Reguladoras de Ácido Ribonucleico / Aprendizaje Profundo Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Brief Bioinform Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Variación Genética / Programas Informáticos / Modelos Estadísticos / Biología Computacional / ARN no Traducido / Secuencias Reguladoras de Ácido Ribonucleico / Aprendizaje Profundo Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Brief Bioinform Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos