Adversarial deconfounding autoencoder for learning robust gene expression embeddings

Gene2vec: distributed representation of genes based on co-expression

(2019) Jingcheng Du et al.   BMC GENOMICS

Exploring single-cell data with deep multitasking neural networks

(2019) Matthew Amodio et al.   NATURE METHODS

Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas

(2015)   NEW ENGLAND JOURNAL OF MEDICINE

Preserving biological heterogeneity with a permuted surrogate variable analysis for genomics batch correction

(2014) Hilary S. Parker et al.   BIOINFORMATICS

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

(2014) Paul Geeleher et al.   GENOME BIOLOGY

The Somatic Genomic Landscape of Glioblastoma

(2013) Cameron W. Brennan et al.   CELL

Inconsistency in large pharmacogenomic studies

(2013) Benjamin Haibe-Kains et al.   NATURE

Batch effect removal methods for microarray gene expression data integration: a survey

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Independent surrogate variable analysis to deconvolve confounding factors in large-scale microarray profiling studies

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A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data

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An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients

(2009) Balazs Györffy et al.   BREAST CANCER RESEARCH AND TREATMENT

Comprehensive genomic characterization defines human glioblastoma genes and core pathways

(2008) Roger McLendon et al.   NATURE

Gene expression–based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study

(2008) Kerby Shedden et al.   NATURE MEDICINE