Elior Rahmani
Elior Rahmani
Verified email at berkeley.edu
Cited by
Cited by
Sparse PCA corrects for cell type heterogeneity in epigenome-wide association studies
E Rahmani, N Zaitlen, Y Baran, C Eng, D Hu, J Galanter, S Oh, ...
Nature methods 13 (5), 443-445, 2016
Genome-wide methylation data mirror ancestry information
E Rahmani, L Shenhav, R Schweiger, P Yousefi, K Huen, B Eskenazi, ...
Epigenetics & chromatin 10 (1), 1-12, 2017
Accurate estimation of cell composition in bulk expression through robust integration of single-cell information
B Jew, M Alvarez, E Rahmani, Z Miao, A Ko, KM Garske, JH Sul, ...
Nature communications 11 (1), 1-11, 2020
Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology
E Rahmani, R Schweiger, B Rhead, LA Criswell, LF Barcellos, E Eskin, ...
Nature communications 10 (1), 1-11, 2019
BayesCCE: a Bayesian framework for estimating cell-type composition from DNA methylation without the need for methylation reference
E Rahmani, R Schweiger, L Shenhav, T Wingert, I Hofer, E Gabel, E Eskin, ...
Genome biology 19 (1), 1-18, 2018
Rheumatoid arthritis naive T cells share hypermethylation sites with synoviocytes
B Rhead, C Holingue, M Cole, X Shao, HL Quach, D Quach, K Shah, ...
Arthritis & Rheumatology 69 (3), 550-559, 2017
GLINT: a user-friendly toolset for the analysis of high-throughput DNA-methylation array data
E Rahmani, R Yedidim, L Shenhav, R Schweiger, O Weissbrod, N Zaitlen, ...
Bioinformatics 33 (12), 1870-1872, 2017
Correcting for cell-type heterogeneity in DNA methylation: a comprehensive evaluation
E Rahmani, N Zaitlen, Y Baran, C Eng, D Hu, J Galanter, S Oh, ...
Nature methods 14 (3), 218-219, 2017
EPIQ—efficient detection of SNP–SNP epistatic interactions for quantitative traits
Y Arkin, E Rahmani, ME Kleber, R Laaksonen, W März, E Halperin
Bioinformatics 30 (12), i19-i25, 2014
RL-SKAT: an exact and efficient score test for heritability and set tests
R Schweiger, O Weissbrod, E Rahmani, M Müller-Nurasyid, S Kunze, ...
Genetics 207 (4), 1275-1283, 2017
A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity
D Goodman-Meza, A Rudas, JN Chiang, PC Adamson, J Ebinger, N Sun, ...
Plos one 15 (9), e0239474, 2020
Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM
M Alvarez, E Rahmani, B Jew, KM Garske, Z Miao, JN Benhammou, ...
Scientific reports 10 (1), 1-16, 2020
Using stochastic approximation techniques to efficiently construct confidence intervals for heritability
R Schweiger, E Fisher, E Rahmani, L Shenhav, S Rosset, E Halperin
Journal of Computational Biology 25 (7), 794-808, 2018
CONFINED: distinguishing biological from technical sources of variation by leveraging multiple methylation datasets
M Thompson, ZJ Chen, E Rahmani, E Halperin
Genome biology 20 (1), 1-15, 2019
Association testing of bisulfite-sequencing methylation data via a Laplace approximation
O Weissbrod, E Rahmani, R Schweiger, S Rosset, E Halperin
Bioinformatics 33 (14), i325-i332, 2017
The causal effect of obesity on prediabetes and insulin resistance reveals the important role of adipose tissue in insulin resistance
Z Miao, M Alvarez, A Ko, Y Bhagat, E Rahmani, B Jew, S Heinonen, ...
PLoS genetics 16 (9), e1009018, 2020
Detecting heritable phenotypes without a model using fast permutation testing for heritability and set-tests
R Schweiger, E Fisher, O Weissbrod, E Rahmani, M Müller-Nurasyid, ...
Nature communications 9 (1), 1-9, 2018
A Bayesian framework for estimating cell type composition from DNA methylation without the need for methylation reference
E Rahmani, R Schweiger, L Shenhav, E Eskin, E Halperin
International Conference on Research in Computational Molecular Biology, 207-223, 2017
Association of a variant in VWA3A with response to anti-vascular endothelial growth factor treatment in neovascular AMD
M Grunin, G Beykin, E Rahmani, R Schweiger, G Barel, S Hagbi-Levi, ...
Investigative ophthalmology & visual science 61 (2), 48-48, 2020
An exact and efficient score test for variance components models
R Schweiger, O Weissbrod, E Rahmani, M Müller-Nurasyid, T Meitinger, ...
bioRxiv, 140889, 2017
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