A systematic mapping study on machine learning techniques for the prediction of CRISPR/Cas9 sgRNA target cleavage.

Dimauro, Giovanni; Barletta, Vita S; Catacchio, Claudia R; Colizzi, Lucio; Maglietta, Rosalia; Ventura, Mario

Dimauro, Giovanni; Barletta, Vita S; Catacchio, Claudia R; Colizzi, Lucio; Maglietta, Rosalia; Ventura, Mario.

Afiliación

Dimauro G; Università degli Studi di Bari, Department of Computer Science, Bari, Italy.
Barletta VS; Università degli Studi di Bari, Department of Computer Science, Bari, Italy.
Catacchio CR; Università degli Studi di Bari, Department of Bioscience, Biotechnology and Environment, Bari, Italy.
Colizzi L; Università degli Studi di Bari, Department of Computer Science, Bari, Italy.
Maglietta R; Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing - National Research Council, Bari, Italy.
Ventura M; Università degli Studi di Bari, Department of Bioscience, Biotechnology and Environment, Bari, Italy.

Comput Struct Biotechnol J ; 20: 5813-5823, 2022.

Article en En | MEDLINE | ID: mdl-36382194

Palabras clave

CRISPR/Cas9; Deep learning; Machine learning; On/Off-target cleavage; sgRNA

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Comput Struct Biotechnol J Año: 2022 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Países Bajos

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