Heejung Shim
Cited by
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A statin-dependent QTL for GATM expression is associated with statin-induced myopathy
LM Mangravite, BE Engelhardt, MW Medina, JD Smith, CD Brown, ...
Nature 502 (7471), 377-380, 2013
A multivariate genome-wide association analysis of 10 LDL subfractions, and their response to statin treatment, in 1868 Caucasians
H Shim, DI Chasman, JD Smith, S Mora, PM Ridker, DA Nickerson, ...
PloS one 10 (4), e0120758, 2015
Thousands of novel translated open reading frames in humans inferred by ribosome footprint profiling
A Raj, SH Wang, H Shim, A Harpak, YI Li, B Engelmann, M Stephens, ...
Elife 5, e13328, 2016
Promoter shape varies across populations and affects promoter evolution and expression noise
IE Schor, JF Degner, D Harnett, E Cannav˛, FP Casale, H Shim, ...
Nature genetics 49 (4), 550-558, 2017
msCentipede: modeling heterogeneity across genomic sites and replicates improves accuracy in the inference of transcription factor binding
A Raj, H Shim, Y Gilad, JK Pritchard, M Stephens
PLoS One 10 (9), e0138030, 2015
Genome-wide association studies using single-nucleotide polymorphisms versus haplotypes: an empirical comparison with data from the North American Rheumatoid Arthritis Consortium
H Shim, H Chun, CD Engelman, BA Payseur
BMC proceedings 3 (S7), S35, 2009
Wavelet-based genetic association analysis of functional phenotypes arising from high-throughput sequencing assays
H Shim, M Stephens
The annals of applied statistics 9 (2), 655, 2015
Integrating quantitative information from ChIP-chip experiments into motif finding
H Shim, S Keleş
Biostatistics 9 (1), 51-65, 2008
BayesCAT: Bayesian co-estimation of alignment and tree
H Shim, B Larget
Biometrics 74 (1), 270-279, 2017
msCentipede: Modeling heterogeneity across genomic sites improves accuracy in the inference of transcription factor binding
A Raj, H Shim, Y Gilad, JK Pritchard, M Stephens
bioRxiv, 012013, 2014
McSplicer: a probabilistic model for estimating splice site usage from RNA-seq data
I Alqassem, Y Sonthalia, E Klitzke, H Shim, S Canzar
bioRxiv, 2020
The impact of normalization methods on RNA-Seq data analysis
M Xiong, S Ajami, F Teimouri, A Akutekwe, H Seker, S Albarqouni, C Baur, ...
Big Data in Omics and Imaging: Integrated Analysis and Causal Inference 20, 1-71, 2018
circuitSNPs: Predicting genetic effects using a Neural Network to model regulatory modules of DNase-seq footprints
AG Shanku, A Findley, C Kalita, H Shim, F Luca, R Pique-Regi
bioRxiv, 337774, 2018
Enhancing motif finding models using multiple sources of genome-wide data
H Shim, O Bembom, S Keles
Web-based Supplementary Materials for “BayesCAT: Bayesian Co-estimation of Alignment and Tree” by
H Shim, B Larget
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