P. Geeleher

发表

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

Paul K. Korir, C. Seoighe, P. Geeleher, 2015, BMC Bioinformatics.

Consistency in large pharmacogenomic studies

Nancy J. Cox, Eric R. Gamazon, Cathal Seoighe, 2016, Nature.

The chemotherapeutic CX-5461 primarily targets TOP2B and exhibits selective activity in high-risk neuroblastoma

Shondra M. Pruett-Miller, Samuel W. Brady, M. Babu, 2021, Nature Communications.

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

R. Grossman, R. S. Huang, Paul Geeleher, 2017, Genome research.

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

E. Gamazon, C. Seoighe, A. Golden, 2012, BMC Genomics.

pRRophetic: An R Package for Prediction of Clinical Chemotherapeutic Response from Tumor Gene Expression Levels

Paul Geeleher, R. S. Huang, J. Barbour, 2014, PloS one.

Shadow Enhancers Are Pervasive Features of Developmental Regulatory Networks

Thomas Zichner, Jan O. Korbel, E. Hilary Gustafson, 2016, Current Biology.

Poly (ADP-ribose) polymerase inhibitor efficacy in head and neck cancer.

R. Weichselbaum, E. Vokes, T. Seiwert, 2014, Oral oncology.

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

R. S. Huang, Adam M. Lee, W. Cho, 2019, Proceedings of the National Academy of Sciences.

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

R. S. Huang, Paul Geeleher, Adam M. Lee, 2019, bioRxiv.

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

J. Easton, Min Pan, R. Chapple, 2021, bioRxiv.

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

N. Cox, R. S. Huang, Paul Geeleher, 2014, Genome Biology.

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

R. S. Huang, N. Cox, P. Geeleher, 2016, Genome Biology.

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

Aaron Golden, Cathal Seoighe, Paul Geeleher, 2013, Bioinform..

Genome-wide interrogation of longitudinal FEV1 in children with asthma.

E. Gamazon, H. Im, M. Ratain, 2014, American journal of respiratory and critical care medicine.

Facilitating Drug Discovery in Breast Cancer by Virtually Screening Patients Using In Vitro Drug Response Modeling

R. S. Huang, G. Greene, Robert F. Gruener, 2021, Cancers.

The impact of microRNA expression on cellular proliferation

E. Gamazon, H. Im, B. LaCroix, 2014, Human Genetics.

Integrative “Omic” Analysis for Tamoxifen Sensitivity through Cell Based Models

R. S. Huang, L. Weng, D. Ziliak, 2014, PloS one.

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

R Stephanie Huang, Aritro Nath, Paul Geeleher, 2019, Briefings Bioinform..

Integrative analyses of genetic variation, epigenetic regulation, and the transcriptome to elucidate the biology of platinum sensitivity

Hae Kyung Im, Paul Geeleher, Nancy J Cox, 2014, BMC Genomics.

Jointly leveraging spatial transcriptomics and deep learning models for pathology image annotation improves cell type identification over either approach alone.

J. Easton, Min Pan, R. Chapple, 2021 .

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

R. Grossman, Paul Geeleher, C. Seoighe, 2018, Genome Biology.

Cancer eQTLs can be determined from heterogeneous tumor gene expression data by modeling variation in tumor purity

R. Grossman, R. S. Huang, C. Seoighe, 2018, bioRxiv.

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

Paul K. Korir, Paul Geeleher, C. Seoighe, 2018 .

Predicting Response to Histone Deacetylase Inhibitors Using High-Throughput Genomics.

M. Maitland, R. S. Huang, A. Loboda, 2015, Journal of the National Cancer Institute.

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

Paul Geeleher, C. Seoighe, M. A. Makrooni, 2022, bioRxiv.

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

N. Cox, R. S. Huang, Paul Geeleher, 2016, Genome Biology.

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

R. S. Huang, R. Huang, N. Cox, 2014 .

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

Shondra M. Pruett-Miller, Taosheng Chen, J. Easton, 2023, bioRxiv.

An unsupervised learning approach uncovers divergent mesenchymal-like gene expression programs across human neuroblastoma tumors, preclinical models, and chemotherapy-exposed tumors

J. Easton, M. Dyer, Min Pan, 2023, bioRxiv.

Abstract 456: Jointly leveraging spatial transcriptomics and deep learning models for image annotation achieves better-than-pathologist performance in cell type identification in tumors

J. Easton, Min Pan, William C. Wright, 2022, Cancer Research.

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

R. Grossman, C. Seoighe, A. Barbeira, 2018, Genome biology.

The impact of microRNA expression on cellular proliferation

E. Gamazon, H. Im, B. LaCroix, 2014, Human Genetics.

pRRophetic R package

P. Geeleher, 2017 .

Jointly leveraging spatial transcriptomics and deep learning models for pathology image annotation improves cell type identification over either approach alone.

Min Pan, R. Chapple, Sivaraman Natarajan, 2021 .