A fusion framework of deep learning and machine learning for predicting sgRNA cleavage efficiency.

Liu, Yu; Fan, Rui; Yi, Jingkun; Cui, Qinghua; Cui, Chunmei

Liu, Yu; Fan, Rui; Yi, Jingkun; Cui, Qinghua; Cui, Chunmei.

Afiliação

Liu Y; Department of Biomedical Informatics, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, Beijing, China.
Fan R; Department of Biomedical Informatics, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, Beijing, China.
Yi J; Department of Biomedical Informatics, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, Beijing, China.
Cui Q; Department of Biomedical Informatics, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, Beijing, China. Electronic address: cuiqinghua@bjmu.edu.cn.
Cui C; Department of Biomedical Informatics, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, Beijing, China. Electronic address: ccm328@bjmu.edu.cn.

Comput Biol Med ; 165: 107476, 2023 10.

Article em En | MEDLINE | ID: mdl-37696181

Assuntos

Aprendizado Profundo; RNA Guia de Sistemas CRISPR-Cas; Aprendizado de Máquina; Redes Neurais de Computação

Palavras-chave

CRISPR/Cas9; Efficiency prediction; Machine learning; sgRNA

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Comput Biol Med Ano de publicação: 2023 Tipo de documento: Article

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