Roberto Olayo-Alarcón

This study shows that human-targeted drugs can both enhance antibiotic resistance and inhibit Campylobacter jejuni growth through efflux pump activation and direct toxicity.

The paper investigates how human-targeted drugs alter regulatory networks in Salmonella Typhimurium, revealing non-antibiotic compounds reshape virulence and antibiotic resistance through specific transcriptional responses.

BATTLE-AMP benchmarks antimicrobial peptide predictors using MIC data, showing that models trained on MIC data outperform binary classifiers and highlighting limitations in distinguishing active from inactive peptides.

The paper systematically characterizes transport regulation in E. coli across 94 chemical cues, revealing new transcriptional responses and the role of regulators like Rob.

This paper introduces a deep learning framework for antimicrobial discovery that identifies new compounds with antimicrobial potential using pre-trained molecular representations.

DGrowthR provides a statistical framework for analyzing large-scale microbial growth data, enabling differential growth analysis and discovery of compounds affecting bacterial growth.

BacSC is a computational pipeline for bacterial single-cell RNA sequencing data that automates quality control, dimension reduction, and clustering, improving subpopulation detection across multiple bacterial species.

The paper introduces interaction models for microbial data to predict community functions and resistance genes, showing improved performance in diverse datasets.

GeDex extracts consensus gene-disease associations from biomedical literature using frequency patterns and supervised learning, achieving competitive performance compared to existing methods.