Christian L. Müller

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.

ritme optimizes feature representation and model selection for microbiome data, improving performance and interpretability across multiple use cases.

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 introduces cosmoDA, a differential abundance testing method for compositional sequencing data that reduces false positives by modeling feature interactions and covariates.

The paper analyzes IgE patterns and gut microbiome data from 508 adults to identify microbial correlates of IgE sensitization, finding specific taxa and metabolic groups associated with different allergen sources.

The paper introduces an open-source ecosystem for microbiome data science using Bioconductor, supporting modular workflows for analyzing complex, multi-modal datasets.

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.

biscot uses optimal transport to integrate and analyze multimodal bacterial single-cell data, showing improved speed and accuracy in disentangling communities and aligning gene expression with cytometry data.

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

The paper introduces VI-MIDAS, a probabilistic framework for analyzing microbiome data, and applies it to Tara Ocean data to identify microbial community modules and associations.

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.

asteRIa models interactions between chromatin modifications and epigenetic readers, identifying cooperative effects on protein binding.

This paper quantifies over-optimism in unsupervised microbiome analysis by comparing results from discovery and validation datasets across three tasks.

The paper introduces a causal inference framework to assess environmental exposure effects on the gut microbiome using observational data from the KORA cohort.

The paper introduces tascCODA, a Bayesian model for analyzing compositional data from amplicon and single-cell sequencing, showing improved performance in identifying compositional changes in microbial and cell type data.

scCODA is a Bayesian model for analyzing compositional single-cell data, improving detection of cell type changes in disease and other stimuli.

The paper introduces a tree-guided aggregation framework for microbiome data to improve predictive modeling by learning data-adaptive taxon groupings.

This study shows shrinkage estimation improves microbial association estimates across different normalization methods using the American Gut Project data.

NetCoMi is an R package that provides a unified workflow for constructing, analyzing, and comparing microbial association networks from high-throughput sequencing data.

The paper introduces a latent graphical model to identify microbial associations while accounting for environmental and technical factors, showing its effectiveness across simulated and gut microbiome datasets.

This paper introduces SPRING, a method for estimating correlations and partial correlations in quantitative microbiome data, and demonstrates its application to real datasets.

This paper introduces 4C-ker, a method that improves the reproducibility of identifying genome-wide interactions from 4C-Seq data by accounting for signal biases and varying resolution.