Osbaldo Reséndis-Antonio

MICOMWeb provides a user-friendly platform for microbial community metabolic modeling of the human gut, addressing challenges in computational infrastructure and user expertise.

The study identifies saliva metabolite changes associated with SARS-CoV-2 infection and disease severity, highlighting potential non-invasive biomarkers for COVID-19.

This study uses machine learning and explainable AI to identify metabolomic signatures and biomarkers in COVID-19 and Post-COVID-19 patients, revealing heterogeneous metabolic responses.

mb-PHENIX denoises microbiota data by combining diffusion and supervised UMAP, improving differential abundance analysis in sparse and noisy count matrices.

This study uses single-cell RNA sequencing to show that CTCF depletion causes transcriptional defects during spermiogenesis, leading to sperm abnormalities and infertility.

The paper introduces sc-PHENIX, an imputation method for scRNA-seq data that addresses over-smoothing issues in diffusion-based approaches by using PCA-UMAP initialization.

The paper identifies gut microbiome biomarkers associated with T2D progression in a Mexican cohort and shows metformin treatment reverses these changes.

This study uses Boolean modelling to analyze the dynamic mechanisms of Pseudomonas aeruginosa quorum sensing and virulence networks, revealing the roles of stochasticity and node connectivity in network dynamics and robustness.

Memote, open-source Python software, runs a community-maintained, partly scored test suite (consistency, biomass, annotations, optional experimental data) on SBML-encoded genome-scale metabolic models, once or through continuous integration.

MICOM models gut microbiota metabolism to predict individual-specific short chain fatty acid production and interactions, showing differences between healthy and diabetic subjects.