José Carbonell‐Caballero

EBEx: an Ensemble-Based Explainable Framework for Gene Calling in Heterogeneous Diseases

EBEx prioritizes disease-relevant genes from transcriptomic data in COPD by integrating data-driven signals with curated gene sets and network-based expansion, identifying biologically relevant candidates and molecular subgroups.

Three-Dimensional Epigenome Roadmap of Human B-cell Differentiation Uncovers Mechanisms of Humoral Immunity and Oncogenesis

This paper creates a 3D epigenomic roadmap of human B-cell differentiation, revealing regulatory mechanisms in immunity and cancer.

Open Benchmarking for Cell-Based Multiscale Models: Lessons from a Community Initiative

This paper presents a community-driven benchmarking initiative for cell-based multiscale models, highlighting variations in tool implementations and advocating for standardized benchmarks and interoperability.

Signalling Network of Breast Cancer Cells in Response to Progesterone

This study maps the dynamic signalling network in breast cancer cells responding to progesterone using antibody microarrays and phosphoproteomics, revealing new proteins and pathway connections.

A cancer pharmacogenomic screen powering crowd-sourced advancement of drug combination prediction

The paper presents a large-scale cancer pharmacogenomic screen with 11,576 experiments on 910 drug combinations across 85 cancer cell lines, enabling crowd-sourced prediction of synergistic drug pairs and biomarkers.

Differential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models

The paper introduces Metabolizer, a web tool that uses gene expression data to analyze metabolic pathway activities and predict therapeutic targets for disease treatment.

Hormone Control Regions mediate opposing steroid receptor-dependent genome organizations

The paper identifies Hormone-Control Regions (HCRs) that organize genome structure and mediate opposing transcriptional responses to steroid hormones in breast cancer cells.

Signaling Pathway Activities Improve Prognosis for Breast Cancer

This study shows that signaling pathway activities are better predictors of breast cancer prognosis than gene-level features and identify additional genes contributing to prognosis beyond pathway analysis.