I created a Metabolomics starter pack. A list of researchers from the wonderful world of #metabolomics. If you would like to be added (or removed) just let me know. go.bsky.app/J3VPYKm
ChemEmbed: a deep learning framework for metabolite identification using enhanced MS/MS data and multidimensional molecular embeddings url: academic.oup.com/bib/article/...
New paper out in Briefings in Bioinformatics
📰SingleFrag: a deep learning tool for MS/MS fragment and spectral prediction and metabolite annotation academic.oup.com/bib/article/...
If you’ve been following #metabolomics literature, you’ve probably seen a lot of debate on in-source fragmentation. We’ve put together a manuscript to clarify what it is, how to deal with it, and what it means for discovery in #metabolomics and #exposomics.
doi.org/10.26434/che...
1/ In MALDI-MSI, matrix deposition is everything. It impacts sensitivity, spatial resolution, and reproducibility. We asked: can we improve matrix application using a dry, solvent-free, controlled method?
2/ We adapted Low-Temperature Thermal Evaporation (LTE)—originally used in nanotechnology and solar cell applications—for matrix deposition in #MALDI-MSI. The result: a reproducible, vacuum-based method that offers precise control over matrix thickness and produces ultra-pure coatings.