CROTON: an automated and variant-aware deep learning framework for predicting CRISPR/Cas9 editing outcomes | Zendy

Victoria R. Li | Zendy; Zijun Zhang | Zendy; Olga G. Troyanskaya | Zendy

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CROTON: an automated and variant-aware deep learning framework for predicting CRISPR/Cas9 editing outcomes

Author(s) -

Victoria R. Li,

Zijun Zhang,

Olga G. Troyanskaya

Publication year - 2021

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btab268

Subject(s) - crispr , computer science , croton , genome editing , convolutional neural network , cas9 , feature engineering , computational biology , feature (linguistics) , artificial intelligence , deep learning , machine learning , gene , biology , genetics , linguistics , philosophy , botany

CRISPR/Cas9 is a revolutionary gene-editing technology that has been widely utilized in biology, biotechnology and medicine. CRISPR/Cas9 editing outcomes depend on local DNA sequences at the target site and are thus predictable. However, existing prediction methods are dependent on both feature and model engineering, which restricts their performance to existing knowledge about CRISPR/Cas9 editing.

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