| Literature DB >> 29431740 |
Hui Kwon Kim1,2, Seonwoo Min3, Myungjae Song1,4, Soobin Jung1,2, Jae Woo Choi1,5, Younggwang Kim1,2, Sangeun Lee1,2, Sungroh Yoon3,6, Hyongbum Henry Kim1,2,5,7,8.
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.Entities:
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Year: 2018 PMID： 29431740 DOI： 10.1038/nbt.4061
Source DB: PubMed Journal: Nat Biotechnol ISSN： 1087-0156 Impact factor: 54.908