Martín Garrido‐Rodríguez

This study uses multi-omics and biophysical phosphoproteomics to analyze BRAFV600E signaling, identifying ETV3 as a new downstream regulator and revealing mechanisms of drug resistance.

This paper introduces HT-PELSA, a high-throughput adaptation of a peptide-centric local stability assay, enabling sensitive protein-ligand profiling in diverse biological samples.

ScAPE predicts drug-induced gene expression changes using a lightweight multitask learning approach, achieving strong performance in a benchmark challenge.

This paper uses time-resolved multi-omics analysis of kidney fibrosis to identify novel mechanisms and validate transcription factors involved in collagen deposition.

This paper evaluates kinase activity inference methods using phosphoproteomics data and benchmarking approaches, finding that curated kinase-substrate libraries significantly impact results.

NetworkCommons integrates prior knowledge, omics data, and network inference methods to build and evaluate context-specific biological networks.

This study shows that Etrinabdione (VCE-004.8) activates B55 to promote angiogenesis and arteriogenesis in critical limb ischemia by enhancing HIF-1 and reducing fibrosis.

The paper introduces CORNETO, a unified framework for knowledge-driven network inference from omics data, improving performance on biological tasks using simulated and real data.

The paper evaluates methods for inferring kinase-driven signaling pathways using phosphoproteomics data and kinase-substrate networks, finding that literature-curated networks with network propagation best recover ground truth interactions.

This study evaluates how different transcriptomics analysis pipelines affect downstream functional enrichment results using FLOP, finding that data filtering significantly impacts gene set correlation.

The paper introduces a high-throughput glycoproteomics method to quantify brain glycoproteome changes induced by gut microbiome composition.

The paper integrates Thermal Proteome Profiling with phosphoproteomic and transcriptomic data using network models to uncover molecular responses to PARP inhibition in ovarian cancer.

The paper compares 15 resources and 6 methods for inferring cell-cell communication from single-cell RNA data, finding significant variation in predicted interactions and biological interpretations.

The paper organizes a DREAM challenge to benchmark predictive models of single-cell signaling using mass cytometry data from breast cancer cell lines.