Anna Goldenberg

The paper analyzes hypothalamic gene expression in mice across puberty, identifying age- and sex-specific changes and their association with pubertal regulation.

The paper develops DEET, a tool that reanalyzes differential gene expression data from multiple studies to help users compare their gene lists with existing results.

This study profiles gene and miRNA expression in mouse pituitaries across postnatal ages to identify sex-biased gene regulation and cell-type proportion differences during development.

The paper introduces a statistical test for detecting heterogeneous effects in omics data and shows its ability to identify disease-relevant genes missed by traditional methods.

scMappR assigns cell-type specificity scores to bulk RNA-seq DEGs using scRNA-seq data and deconvolution methods, showing accurate assignment in kidney regeneration analysis.

The paper integrates brain imaging and behavioral data to identify transdiagnostic subgroups in children with ASD, ADHD, or OCD, finding stable groups based on cortical thickness and inattention symptoms.

The paper introduces KuLGaP, a selective measure to assess therapy response in patient-derived xenografts by comparing treatment and control arms, improving accuracy over existing methods.

The paper develops Xeva, an open-source tool for analyzing PDX pharmacogenomic data, finding that few genes and pathways show passage-specific changes.

This paper estimates DNA methylation networks using a sparse latent Gaussian graphical model and shows it explains gene expression patterns in unseen datasets.

This paper reanalyzes pharmacogenomic data from GDSC and CCLE, finding continued inconsistency in drug sensitivity measures despite improved statistical methods and expanded datasets.

This paper introduces DNF, a computational framework that classifies new drugs by integrating structural, perturbation, and sensitivity data to infer drug relationships and taxonomies.

The paper reanalyzes pharmacogenomic data from GDSC and CCLE, finding continued inconsistency in drug response measures despite claims of agreement.

The paper compares drug response prediction models using cell lines, patients, and integrated data, finding that integrative models outperform cell line-only models and require fewer patients.