Personal transcriptome variation is poorly explained by current genomic deep learning models.

Huang, Connie; Shuai, Richard W; Baokar, Parth; Chung, Ryan; Rastogi, Ruchir; Kathail, Pooja; Ioannidis, Nilah M

Huang, Connie; Shuai, Richard W; Baokar, Parth; Chung, Ryan; Rastogi, Ruchir; Kathail, Pooja; Ioannidis, Nilah M.

Afiliação

Huang C; Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, CA, USA.
Shuai RW; Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, CA, USA.
Baokar P; Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, CA, USA.
Chung R; Center for Computational Biology, University of California Berkeley, Berkeley, CA, USA.
Rastogi R; Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, CA, USA.
Kathail P; Center for Computational Biology, University of California Berkeley, Berkeley, CA, USA.
Ioannidis NM; Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, CA, USA. nilah@berkeley.edu.

Nat Genet ; 55(12): 2056-2059, 2023 Dec.

Article em En | MEDLINE | ID: mdl-38036790

Assuntos

Aprendizado Profundo; Transcriptoma; Humanos; Transcriptoma/genética; Variação Genética/genética; Genoma; Genômica

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Transcriptoma / Aprendizado Profundo Limite: Humans Idioma: En Revista: Nat Genet Assunto da revista: GENETICA MEDICA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Estados Unidos

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