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

Ramzan Umarov

Showing results (1-10 of 11) with videos related to

Pageof 2
Sort By:
Biochemical Society Transactions|October 13, 2023
Enhancer target prediction: state-of-the-art approaches and future prospectsRamzan Umarov, Chung-Chau Hon
Plos Computational Biology|October 5, 2021
DeepCellState: An autoencoder-based framework for predicting cell type specific transcriptional states induced by drug treatmentRamzan Umarov, Yu Li, Erik Arner
Synthetic Biology (Oxford, England)|September 30, 2020
ACRE: Absolute concentration robustness exploration in module-based combinatorial networksHiroyuki Kuwahara, Ramzan Umarov, Islam Almasri, et al.
Bioinformatics (Oxford, England)|January 3, 2019
Promoter analysis and prediction in the human genome using sequence-based deep learning modelsRamzan Umarov, Hiroyuki Kuwahara, Yu Li, et al.
Bioinformatics (Oxford, England)|September 30, 2017
Sequence2Vec: a novel embedding approach for modeling transcription factor binding affinity landscapeHanjun Dai, Ramzan Umarov, Hiroyuki Kuwahara, et al.
Plos Computational Biology|September 7, 2021
ReFeaFi: Genome-wide prediction of regulatory elements driving transcription initiationRamzan Umarov, Yu Li, Takahiro Arakawa, et al.
ACS Synthetic Biology|January 13, 2017
SBOLme: a Repository of SBOL Parts for Metabolic EngineeringHiroyuki Kuwahara, Xuefeng Cui, Ramzan Umarov, et al.
Bioinformatics (Oxford, England)|October 26, 2017
DEEPre: sequence-based enzyme EC number prediction by deep learningYu Li, Sheng Wang, Ramzan Umarov, et al.
Microbiome|February 9, 2021
HMD-ARG: hierarchical multi-task deep learning for annotating antibiotic resistance genesYu Li, Zeling Xu, Wenkai Han, et al.
Nature Communications|November 1, 2019
A deep learning framework to predict binding preference of RNA constituents on protein surfaceJordy Homing Lam, Yu Li, Lizhe Zhu, et al.
Pageof 2

Showing results (1-10 of 11) with videos related to

Sort By:
Pageof 2
Biochemical Society Transactions|October 13, 2023
Enhancer target prediction: state-of-the-art approaches and future prospectsRamzan Umarov, Chung-Chau Hon
Plos Computational Biology|October 5, 2021
DeepCellState: An autoencoder-based framework for predicting cell type specific transcriptional states induced by drug treatmentRamzan Umarov, Yu Li, Erik Arner
Synthetic Biology (Oxford, England)|September 30, 2020
ACRE: Absolute concentration robustness exploration in module-based combinatorial networksHiroyuki Kuwahara, Ramzan Umarov, Islam Almasri, et al.
Bioinformatics (Oxford, England)|January 3, 2019
Promoter analysis and prediction in the human genome using sequence-based deep learning modelsRamzan Umarov, Hiroyuki Kuwahara, Yu Li, et al.
Bioinformatics (Oxford, England)|September 30, 2017
Sequence2Vec: a novel embedding approach for modeling transcription factor binding affinity landscapeHanjun Dai, Ramzan Umarov, Hiroyuki Kuwahara, et al.
Plos Computational Biology|September 7, 2021
ReFeaFi: Genome-wide prediction of regulatory elements driving transcription initiationRamzan Umarov, Yu Li, Takahiro Arakawa, et al.
ACS Synthetic Biology|January 13, 2017
SBOLme: a Repository of SBOL Parts for Metabolic EngineeringHiroyuki Kuwahara, Xuefeng Cui, Ramzan Umarov, et al.
Bioinformatics (Oxford, England)|October 26, 2017
DEEPre: sequence-based enzyme EC number prediction by deep learningYu Li, Sheng Wang, Ramzan Umarov, et al.
Microbiome|February 9, 2021
HMD-ARG: hierarchical multi-task deep learning for annotating antibiotic resistance genesYu Li, Zeling Xu, Wenkai Han, et al.
Nature Communications|November 1, 2019
A deep learning framework to predict binding preference of RNA constituents on protein surfaceJordy Homing Lam, Yu Li, Lizhe Zhu, et al.
Pageof 2