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Bioinformatics (Oxford, England)|June 27, 2022
DeepCRISTL: deep transfer learning to predict CRISPR/Cas9 functional and endogenous on-target editing efficiencyShai Elkayam, Yaron OrensteinBioinformatics (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 OrensteinJournal of Computational Biology : a Journal of Computational Molecular Cell Biology|January 30, 2020
Reverse de Bruijn: Utilizing Reverse Peptide Synthesis to Cover All Amino Acid k-mersYaron OrensteinMethods in Molecular Biology (Clifton, N.J.)|February 19, 2021
Improved Analysis of High-Throughput Sequencing Data Using Small Universal k-Mer Hitting SetsYaron OrensteinBriefings in Bioinformatics|May 21, 2021
A comparative analysis of RNA-binding proteins binding models learned from RNAcompete, RNA Bind-n-Seq and eCLIP dataEitamar Tripto, Yaron OrensteinNucleic Acids Research|February 7, 2014
A comparative analysis of transcription factor binding models learned from PBM, HT-SELEX and ChIP dataYaron Orenstein, Ron ShamirNucleic Acids Research|May 30, 2024
Generating, modeling and evaluating a large-scale set of CRISPR/Cas9 off-target sites with bulgesOfir Yaish, Yaron OrensteinBioinformatics (Oxford, England)|December 31, 2020
DeepSELEX: inferring DNA-binding preferences from HT-SELEX data using multi-class CNNsMaor Asif, Yaron OrensteinBriefings 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 OrensteinPageof 6