한빛사 논문
- 조회2,513
서울대학교
Hui Kwon Kim1,2,9, Seonwoo Min3,9, 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,*
1Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea. 2Brain Korea 21 Plus Project for Medical Sciences, Yonsei University College of Medicine, Seoul, Republic of Korea. 3Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea. 4Graduate School of Biomedical Science and Engineering, Hanyang University, Seoul, Republic of Korea. 5Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Republic of Korea. 6Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea. 7Center for Nanomedicine, Institute for Basic Science (IBS), Seoul, Republic of Korea. 8Yonsei-IBS Institute, Yonsei University, Seoul, Republic of Korea. 9These authors contributed equally to this work.
*Correspondence should be addressed to H.K. or S.Y..
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.
논문정보
TOP52018년 선정
- Research article
- 2018년 01월 (BRIC 등록일 2018-01-30)
- 국내 연구진
- 의약학 > 유전 및 유전체의학
- 60회 이상 인용된 논문
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