Article

Machine learning methods for predicting guide RNA effects in CRISPR epigenome editing experiments

2024-04-19

Abstract excerpt

CRISPR epigenomic editing technologies enable functional interrogation of non-coding elements. However, current computational methods for guide RNA (gRNA) design do not effectively predict the power potential, molecular and cellular impact to optimize for efficient gRNAs, which are crucial for successful applications of these technologies. We present “launch-dCas9” (machine LeArning based UNified CompreHensive fra...

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Literature Corpus work: 658022a2-f650-5e9b-9132-160047a2a7e7
DOI: 10.1101/2024.04.18.590188

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