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나노의학 연구단
나노의학 연구단
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Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity Highly Cited Paper

Cited 5 time in webofscience Cited 0 time in scopus
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Title
Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity
Author(s)
Hui Kwon Kim; Seonwoo Min; Myungjae Song; Soobin Jung; Jae Woo Choi; Younggwang Kim; Sangeun Lee; Sungroh Yoon; Hyongbum Kim
Publication Date
2018-03
Journal
NATURE BIOTECHNOLOGY, v.36, no.3, pp.239 - +
Publisher
NATURE PUBLISHING GROUP
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
URI
https://pr.ibs.re.kr/handle/8788114/4612
ISSN
1087-0156
Appears in Collections:
Center for Nanomedicine (나노의학 연구단) > Journal Papers
Files in This Item:
31.Nature Biotechnology 36, 239-241, (2018).pdfDownload

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