Jose Lugo-Martinez

SpaTRACE jointly infers gene regulatory networks and cell-cell signaling from spatial transcriptomics data, outperforming existing methods in synthetic benchmarks and revealing novel interactions in development and regeneration.

This paper introduces DeepSAS, a deep graph framework to identify senescent cells and their associated genes, revealing cell-type-specific senescence features in aging and disease.

This paper combines topological data analysis and geometric deep learning to analyze protein pockets and show that integrating local and global representations improves enzyme classification.

The paper evaluates variant effect predictions for ARSA gene variants, finding that a bootcamp-trained model outperformed others and that deep learning slightly improved performance over simpler methods.

The paper improves protein interaction prediction by addressing incomplete features and proteome coverage, identifying cell line-specific complexes.

scResolve recovers single-cell expression profiles from multi-cellular spatial transcriptomics data, enabling cell type-specific analysis and rare population detection in human tissues.

The paper introduces a computational pipeline, PALM, that integrates multi-omics time-series microbiome data using dynamic Bayesian networks to identify microbial and host interactions.

MutPred2 identifies pathogenic amino acid variants, predicts their molecular mechanisms, and validates their functional impact in neurodevelopmental disorders.

The paper reconstructs signaling and regulatory networks for SARS-CoV-2 infection and identifies potential drug targets by integrating multi-omics data and experimental validation.