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Jae Hoon Sul

Showing results (11-20 of 62) with videos related to

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HGG Advances|August 14, 2023
Building an optimal predictive model for imputing tissue-specific gene expression by combining genotype and whole-blood transcriptome dataSunwoo Jung, Cue Hyunkyu Lee, Jae Hoon Sul, et al.
Plos Genetics|June 21, 2013
Effectively identifying eQTLs from multiple tissues by combining mixed model and meta-analytic approachesJae Hoon Sul, Buhm Han, Chun Ye, et al.
Journal of Computational Biology : a Journal of Computational Molecular Cell Biology|April 15, 2015
Gene-Gene Interactions Detection Using a Two-stage ModelZhanyong Wang, Jae Hoon Sul, Sagi Snir, et al.
Genome Biology|April 9, 2014
Effectively identifying regulatory hotspots while capturing expression heterogeneity in gene expression studiesJong Wha J Joo, Jae Hoon Sul, Buhm Han, et al.
HGG Advances|May 23, 2022
Polygenic risk scores of endo-phenotypes identify the effect of genetic background in congenital heart diseaseSarah J Spendlove, Leroy Bondhus, Gentian Lluri, et al.
Bioinformatics (Oxford, England)|June 17, 2016
Using genomic annotations increases statistical power to detect eGenesDat Duong, Jennifer Zou, Farhad Hormozdiari, et al.
Human Molecular Genetics|February 25, 2016
A general framework for meta-analyzing dependent studies with overlapping subjects in association mappingBuhm Han, Dat Duong, Jae Hoon Sul, et al.
Plos Computational Biology|December 19, 2019
ForestQC: Quality control on genetic variants from next-generation sequencing data using random forestJiajin Li, Brandon Jew, Lingyu Zhan, et al.
Nature Communications|July 12, 2024
CoPheScan: phenome-wide association studies accounting for linkage disequilibriumIchcha Manipur, Guillermo Reales, Jae Hoon Sul, et al.
Bioinformatics (Oxford, England)|September 9, 2017
Applying meta-analysis to genotype-tissue expression data from multiple tissues to identify eQTLs and increase the number of eGenesDat Duong, Lisa Gai, Sagi Snir, et al.
Pageof 7

Showing results (11-20 of 62) with videos related to

Sort By:
Pageof 7
HGG Advances|August 14, 2023
Building an optimal predictive model for imputing tissue-specific gene expression by combining genotype and whole-blood transcriptome dataSunwoo Jung, Cue Hyunkyu Lee, Jae Hoon Sul, et al.
Plos Genetics|June 21, 2013
Effectively identifying eQTLs from multiple tissues by combining mixed model and meta-analytic approachesJae Hoon Sul, Buhm Han, Chun Ye, et al.
Journal of Computational Biology : a Journal of Computational Molecular Cell Biology|April 15, 2015
Gene-Gene Interactions Detection Using a Two-stage ModelZhanyong Wang, Jae Hoon Sul, Sagi Snir, et al.
Genome Biology|April 9, 2014
Effectively identifying regulatory hotspots while capturing expression heterogeneity in gene expression studiesJong Wha J Joo, Jae Hoon Sul, Buhm Han, et al.
HGG Advances|May 23, 2022
Polygenic risk scores of endo-phenotypes identify the effect of genetic background in congenital heart diseaseSarah J Spendlove, Leroy Bondhus, Gentian Lluri, et al.
Bioinformatics (Oxford, England)|June 17, 2016
Using genomic annotations increases statistical power to detect eGenesDat Duong, Jennifer Zou, Farhad Hormozdiari, et al.
Human Molecular Genetics|February 25, 2016
A general framework for meta-analyzing dependent studies with overlapping subjects in association mappingBuhm Han, Dat Duong, Jae Hoon Sul, et al.
Plos Computational Biology|December 19, 2019
ForestQC: Quality control on genetic variants from next-generation sequencing data using random forestJiajin Li, Brandon Jew, Lingyu Zhan, et al.
Nature Communications|July 12, 2024
CoPheScan: phenome-wide association studies accounting for linkage disequilibriumIchcha Manipur, Guillermo Reales, Jae Hoon Sul, et al.
Bioinformatics (Oxford, England)|September 9, 2017
Applying meta-analysis to genotype-tissue expression data from multiple tissues to identify eQTLs and increase the number of eGenesDat Duong, Lisa Gai, Sagi Snir, et al.
Pageof 7