Timothy M. D. Ebbels

Simulated metabolic profiles reveal biases in pathway analysis methods

This study simulates metabolic profiles to evaluate pathway analysis methods, revealing biases when applied to metabolomics data.

PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration

PathIntegrate integrates multi-omics data using pathway-based modeling to identify important pathways and molecules contributing to outcomes, demonstrated on COPD and COVID-19 datasets.

Genome scale metabolic network modelling for metabolic profile predictions

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.

LAVASET: Latent Variable Stochastic Ensemble of Trees. A novel ensemble method for correlated datasets.

LAVASET improves feature importance estimation in correlated datasets by incorporating correlation factors, achieving non-inferior prediction accuracy compared to Random Forests.

Single sample pathway analysis in metabolomics: performance evaluation and application

This paper evaluates single-sample pathway analysis methods for metabolomics, finding that clustering-based methods offer higher precision while GSEA-based methods have better recall, and introduces two new methods with a software package.

Pathway analysis in metabolomics: pitfalls and best practice for the use of over-representation analysis

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

Estimation of permutation-based metabolome-wide significance thresholds

The paper estimates permutation-based metabolome-wide significance thresholds using parametric approximations and permutation procedures to control type I error rates in metabolome-wide association studies.

PhenoMeNal: Processing and analysis of Metabolomics data in the Cloud

PhenoMeNal provides a cloud-based platform for metabolomics data analysis, integrating tools and workflows to enable reproducible and scalable research.