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pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels

PloS one, 2014-09, Vol.9 (9), p.e107468-e107468

2014

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Shadow Enhancers Are Pervasive Features of Developmental Regulatory Networks

Current biology, 2016-01, Vol.26 (1), p.38-51

2016

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Gene-set analysis is severely biased when applied to genome-wide methylation data

Bioinformatics, 2013-08, Vol.29 (15), p.1851-1857

2013

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Random-effects meta-analysis of effect sizes as a unified framework for gene set analysis

PLoS computational biology, 2022-10, Vol.18 (10), p.e1010278-e1010278

2022

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Cancer expression quantitative trait loci (eQTLs) can be determined from heterogeneous tumor gene expression data by modeling variation in tumor purity

Genome Biology, 2018-09, Vol.19 (1), p.130-14, Article 130

2018

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A CRISPR-drug perturbational map for identifying compounds to combine with commonly used chemotherapeutics

Nature communications, 2023-11, Vol.14 (1), p.7332-7332, Article 7332

2023

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An integrated single-cell RNA-seq map of human neuroblastoma tumors and preclinical models uncovers divergent mesenchymal-like gene expression programs

Genome Biology, 2024-06, Vol.25 (1), p.161-26, Article 161

2024

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The regulatory effect of miRNAs is a heritable genetic trait in humans

BMC genomics, 2012-08, Vol.13 (1), p.383-383, Article 383

2012

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Integrative "omic" analysis for tamoxifen sensitivity through cell based models

PloS one, 2014-04, Vol.9 (4), p.e93420

2014

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Abstract 5124: A CRISPR-drug perturbational map for identifying compounds to combine with commonly used chemotherapeutics

Cancer research (Chicago, Ill.), 2024-03, Vol.84 (6_Supplement), p.5124-5124

2024

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Abstract 4936: An ultrahigh-throughput synergy screening platform enables discovery of novel drug combinations

Cancer research (Chicago, Ill.), 2024-03, Vol.84 (6_Supplement), p.4936-4936

2024

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Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines

Genome biology, 2014-03, Vol.15 (3), p.R47-R47, Article R47

2014

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Abstract 3481: Leveraging protein coding gene expression profiles to accurately impute lncRNA transcriptome of cancer cells

Cancer research (Chicago, Ill.), 2017-07, Vol.77 (13_Supplement), p.3481-3481

2017

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Long non-coding RNA transcriptome of uncharacterized samples can be accurately imputed using protein-coding genes

Briefings in bioinformatics, 2020-03, Vol.21 (2), p.637-648

2020

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Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies

Genome research, 2017-10, Vol.27 (10), p.1743-1751

2017

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Cancer biomarker discovery is improved by accounting for variability in general levels of drug sensitivity in pre-clinical models

Genome Biology, 2016-09, Vol.17 (1), p.190-190, Article 190

2016

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Cell type identification in spatial transcriptomics data can be improved by leveraging cell-type-informative paired tissue images using a Bayesian probabilistic model

Nucleic acids research, 2022-08, Vol.50 (14), p.e80-e80

2022

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Discovering long noncoding RNA predictors of anticancer drug sensitivity beyond protein-coding genes

Proceedings of the National Academy of Sciences - PNAS, 2019-10, Vol.116 (44), p.22020-22029

2019

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Consistency in large pharmacogenomic studies

Nature (London), 2016-12, Vol.540 (7631), p.E1-E2

2016

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Institutional Profile: Pharmacogenomic research in R Stephanie Huang Laboratory

Pharmacogenomics, 2017-04, Vol.18 (6), p.519-522

2017

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Abstract 3578: Comprehensive pharmacogenomic analysis establishes lncRNAs as protein-coding independent biomarker of drug response in human cancers

Cancer research (Chicago, Ill.), 2019-07, Vol.79 (13_Supplement), p.3578-3578

2019

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Seq-ing improved gene expression estimates from microarrays using machine learning

BMC bioinformatics, 2015-09, Vol.16 (1), p.286-286, Article 286

2015

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Exploring the Link between the Germline and Somatic Genome in Cancer

Cancer discovery, 2017-04, Vol.7 (4), p.354-355

2017

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Abstract 5561: Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines

Cancer research (Chicago, Ill.), 2014-10, Vol.74 (19_Supplement), p.5561-5561

2014

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Abstract 4075: Meta-analysis of neuroblastoma single cell RNA-seq datasets identifies conserved and divergent gene expression programs across human and preclinical models

Cancer research (Chicago, Ill.), 2022-06, Vol.82 (12_Supplement), p.4075-4075

2022

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pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels

Shadow Enhancers Are Pervasive Features of Developmental Regulatory Networks

Gene-set analysis is severely biased when applied to genome-wide methylation data

Random-effects meta-analysis of effect sizes as a unified framework for gene set analysis

Cancer expression quantitative trait loci (eQTLs) can be determined from heterogeneous tumor gene expression data by modeling variation in tumor purity

A CRISPR-drug perturbational map for identifying compounds to combine with commonly used chemotherapeutics

An integrated single-cell RNA-seq map of human neuroblastoma tumors and preclinical models uncovers divergent mesenchymal-like gene expression programs

The regulatory effect of miRNAs is a heritable genetic trait in humans

Integrative "omic" analysis for tamoxifen sensitivity through cell based models

Abstract 5124: A CRISPR-drug perturbational map for identifying compounds to combine with commonly used chemotherapeutics

Abstract 4936: An ultrahigh-throughput synergy screening platform enables discovery of novel drug combinations

Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines

Abstract 3481: Leveraging protein coding gene expression profiles to accurately impute lncRNA transcriptome of cancer cells

Long non-coding RNA transcriptome of uncharacterized samples can be accurately imputed using protein-coding genes

Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies

Cancer biomarker discovery is improved by accounting for variability in general levels of drug sensitivity in pre-clinical models

Cell type identification in spatial transcriptomics data can be improved by leveraging cell-type-informative paired tissue images using a Bayesian probabilistic model

Discovering long noncoding RNA predictors of anticancer drug sensitivity beyond protein-coding genes

Consistency in large pharmacogenomic studies

Institutional Profile: Pharmacogenomic research in R Stephanie Huang Laboratory

Abstract 3578: Comprehensive pharmacogenomic analysis establishes lncRNAs as protein-coding independent biomarker of drug response in human cancers

Seq-ing improved gene expression estimates from microarrays using machine learning

Exploring the Link between the Germline and Somatic Genome in Cancer

Abstract 5561: Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines

Abstract 4075: Meta-analysis of neuroblastoma single cell RNA-seq datasets identifies conserved and divergent gene expression programs across human and preclinical models

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