Pablo Rodríguez-Mier

The paper benchmarks generative models for synthetic bulk RNA-seq data, finding trade-offs between utility, biological preservation, and privacy risks across different methods.

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

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

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.

The paper introduces SAMBA, a computational method that predicts metabolite changes under metabolic perturbations using genome-scale metabolic network modeling and compares predictions with experimental data.

LIANA+ is a framework for analyzing cell-cell communication in single-cell and spatial transcriptomics data, integrating multiple methods and supporting multi-omics data.

The paper identifies disease associations for 1,047 overlooked metabolites by extending the guilt by association principle using a Bayesian framework, resulting in 35,000 new associations.

This paper evaluates over-representation analysis (ORA) in metabolomics, showing how different parameters and datasets affect results and offering best practices for its use.

This paper introduces DEXOM, a method to enumerate diverse optimal metabolic networks from multi-omics data to improve in-silico gene essentiality predictions in yeast.