Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Filters

Seonwoo Min

Showing results (1-10 of 18) with videos related to

Pageof 2
Sort By:
Bioinformatics (Oxford, England)|October 22, 2021
TargetNet: functional microRNA target prediction with deep neural networksSeonwoo Min, Byunghan Lee, Sungroh Yoon
Briefings in Bioinformatics|July 31, 2016
Deep learning in bioinformaticsSeonwoo Min, Byunghan Lee, Sungroh Yoon
Plos One|May 18, 2021
Protein transfer learning improves identification of heat shock protein familiesSeonwoo Min, HyunGi Kim, Byunghan Lee, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics|August 19, 2020
DNA Privacy: Analyzing Malicious DNA Sequences Using Deep Neural NetworksHo Bae, Seonwoo Min, Hyun-Soo Choi, et al.
Nature Biotechnology|February 8, 2024
Author Correction: Predicting the efficiency of prime editing guide RNAs in human cellsHui Kwon Kim, Goosang Yu, Jinman Park, et al.
Nature Biotechnology|September 22, 2020
Predicting the efficiency of prime editing guide RNAs in human cellsHui Kwon Kim, Goosang Yu, Jinman Park, et al.
Nature Biotechnology|February 13, 2018
Deep learning improves prediction of CRISPR-Cpf1 guide RNA activityHui Kwon Kim, Seonwoo Min, Myungjae Song, et al.
Nature Biotechnology|May 15, 2023
Deep learning models to predict the editing efficiencies and outcomes of diverse base editorsNahye Kim, Sungchul Choi, Sungjae Kim, et al.
Science Advances|November 15, 2019
SpCas9 activity prediction by DeepSpCas9, a deep learning-based model with high generalization performanceHui Kwon Kim, Younggwang Kim, Sungtae Lee, et al.
Nature Biotechnology|June 10, 2020
Prediction of the sequence-specific cleavage activity of Cas9 variantsNahye Kim, Hui Kwon Kim, Sungtae Lee, et al.
Pageof 2

Showing results (1-10 of 18) with videos related to

Sort By:
Pageof 2
Bioinformatics (Oxford, England)|October 22, 2021
TargetNet: functional microRNA target prediction with deep neural networksSeonwoo Min, Byunghan Lee, Sungroh Yoon
Briefings in Bioinformatics|July 31, 2016
Deep learning in bioinformaticsSeonwoo Min, Byunghan Lee, Sungroh Yoon
Plos One|May 18, 2021
Protein transfer learning improves identification of heat shock protein familiesSeonwoo Min, HyunGi Kim, Byunghan Lee, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics|August 19, 2020
DNA Privacy: Analyzing Malicious DNA Sequences Using Deep Neural NetworksHo Bae, Seonwoo Min, Hyun-Soo Choi, et al.
Nature Biotechnology|February 8, 2024
Author Correction: Predicting the efficiency of prime editing guide RNAs in human cellsHui Kwon Kim, Goosang Yu, Jinman Park, et al.
Nature Biotechnology|September 22, 2020
Predicting the efficiency of prime editing guide RNAs in human cellsHui Kwon Kim, Goosang Yu, Jinman Park, et al.
Nature Biotechnology|February 13, 2018
Deep learning improves prediction of CRISPR-Cpf1 guide RNA activityHui Kwon Kim, Seonwoo Min, Myungjae Song, et al.
Nature Biotechnology|May 15, 2023
Deep learning models to predict the editing efficiencies and outcomes of diverse base editorsNahye Kim, Sungchul Choi, Sungjae Kim, et al.
Science Advances|November 15, 2019
SpCas9 activity prediction by DeepSpCas9, a deep learning-based model with high generalization performanceHui Kwon Kim, Younggwang Kim, Sungtae Lee, et al.
Nature Biotechnology|June 10, 2020
Prediction of the sequence-specific cleavage activity of Cas9 variantsNahye Kim, Hui Kwon Kim, Sungtae Lee, et al.
Pageof 2