김형범 (Hyongbum Kim) 연세대학교 의과대학, 기초과학연구원
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Nat. Biotechnol., volume 36, pages 239–241 (2018). Published online:29 January 2018, doi:10.1038/nbt.4061
Deep learning improves prediction of CRISPR–Cpf1 guide RNA activity
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Abstract
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.
Category: Biotechnology
등록일 2018.01.30
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