Reza M. Salek

The paper introduces mzQC, a standard for representing quality control metrics in mass spectrometry, enabling interoperable and machine-readable QC reporting.

This paper integrates metabolomics data with machine learning techniques to improve cancer detection and biomarker discovery by comparing multiple integration strategies.

The paper introduces a pipeline to normalize and pool metabolomics data across studies, improving comparability and reducing inter-study variability.

The paper benchmarks quality assurance strategies for non-targeted metabolomics data pre-processing using publicly available datasets.

This paper develops a machine learning classifier to automate biomarker curation in the Exposome-Explorer database, reducing manual screening by 90% with 70.1% F2-score.

The paper introduces a microservices-based framework for scalable data analysis, validated in metabolomics, enabling dynamic scaling and integration of software tools.

This paper integrates Galaxy with Kubernetes to enable scalable, cloud-agnostic bioinformatics workflows.

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

The paper introduces the Omics Discovery Index, a platform to search and link public omics datasets across different fields, addressing challenges in data organization and accessibility.