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JOURNAL ARTICLE
RESEARCH SUPPORT, NON-U.S. GOV'T
Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity.
Nature Biotechnology 2018 March
We present two algorithms to predict the activity of AsCpf1 guide RNAs. Indel frequencies for 15,000 target sequences were used in a deep-learning framework based on a convolutional neural network to train Seq-deepCpf1. We then incorporated chromatin accessibility information to create the better-performing DeepCpf1 algorithm for cell lines for which such information is available and show that both algorithms outperform previous machine learning algorithms on our own and published data sets.
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