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Briefings in Functional Genomics|August 8, 2016
Modeling protein-DNA binding via high-throughput in vitro technologiesYaron Orenstein, Ron ShamirBioinformatics (Oxford, England)|July 7, 2026
CROP: a feature-independent context-aware method for CRISPR-Cas9 frameshift predictionIdo Tziony, Yaron OrensteinJournal of Computational Biology : a Journal of Computational Molecular Cell Biology|December 30, 2015
Efficient Design of Compact Unstructured RNA Libraries Covering All k-mersYaron Orenstein, Bonnie BergerBioinformatics (Oxford, England)|July 2, 2013
Design of shortest double-stranded DNA sequences covering all k-mers with applications to protein-binding microarrays and synthetic enhancersYaron Orenstein, Ron ShamirBioinformatics (Oxford, England)|September 20, 2022
DeepZF: improved DNA-binding prediction of C2H2-zinc-finger proteins by deep transfer learningSofia Aizenshtein-Gazit, Yaron OrensteinBriefings in Bioinformatics|July 12, 2023
An overview on nucleic-acid G-quadruplex prediction: from rule-based methods to deep neural networksKarin Elimelech-Zohar, Yaron OrensteinIEEE/ACM Transactions on Computational Biology and Bioinformatics|April 19, 2021
G4detector: Convolutional Neural Network to Predict DNA G-QuadruplexesMira Barshai, Alice Aubert, Yaron OrensteinBioinformatics (Oxford, England)|June 17, 2016
RCK: accurate and efficient inference of sequence- and structure-based protein-RNA binding models from RNAcompete dataYaron Orenstein, Yuhao Wang, Bonnie BergerBMC Genomics|February 22, 2018
Finding RNA structure in the unstructured RBPomeYaron Orenstein, Uwe Ohler, Bonnie BergerBioinformatics (Oxford, England)|July 29, 2024
DeepCRISTL: deep transfer learning to predict CRISPR/Cas9 on-target editing efficiency in specific cellular contextsShai Elkayam, Ido Tziony, Yaron OrensteinPageof 6