Sean Colby

The paper proposes identification probability as an automated and transferable method to assess metabolite identification confidence in metabolomics.

This paper introduces molecular hypernetworks to improve annotation in metabolomics data by representing complex relationships beyond traditional molecular networks.

MerCat2 analyzes omics data properties through k-mer counting and diversity metrics without requiring a database.

The paper improves compound identification and inter-laboratory reproducibility in untargeted lipidomics using harmonized methods and spectral analysis.