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Frontiers in Bioinformatics|October 28, 2022
The Effect of Model Directionality on Cell-Type-Specific Differential DNA Methylation AnalysisElior Rahmani, Brandon Jew, Eran HalperinGenome Biology|October 4, 2025
Unico: a unified model for cell-type resolution genomics from heterogeneous omics dataZeyuan Johnson Chen, Elior Rahmani, Eran HalperinGenome Biology|July 14, 2019
CONFINED: distinguishing biological from technical sources of variation by leveraging multiple methylation datasetsMike Thompson, Zeyuan Johnson Chen, Elior Rahmani, et al.Bioinformatics (Oxford, England)|September 9, 2017
Association testing of bisulfite-sequencing methylation data via a Laplace approximationOmer Weissbrod, Elior Rahmani, Regev Schweiger, et al.Nature Communications|April 26, 2020
Accurate estimation of cell composition in bulk expression through robust integration of single-cell informationBrandon Jew, Marcus Alvarez, Elior Rahmani, et al.Nature Communications|June 5, 2020
Publisher Correction: Accurate estimation of cell composition in bulk expression through robust integration of single-cell informationBrandon Jew, Marcus Alvarez, Elior Rahmani, et al.Journal of Computational Biology : a Journal of Computational Molecular Cell Biology|June 23, 2018
Using Stochastic Approximation Techniques to Efficiently Construct Confidence Intervals for HeritabilityRegev Schweiger, Eyal Fisher, Elior Rahmani, et al.Scientific Reports|July 5, 2020
Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEMMarcus Alvarez, Elior Rahmani, Brandon Jew, et al.Bioinformatics (Oxford, England)|June 17, 2014
EPIQ-efficient detection of SNP-SNP epistatic interactions for quantitative traitsYa'ara Arkin, Elior Rahmani, Marcus E Kleber, et al.Bioinformatics (Oxford, England)|February 9, 2017
GLINT: a user-friendly toolset for the analysis of high-throughput DNA-methylation array dataElior Rahmani, Reut Yedidim, Liat Shenhav, et al.Pageof 26