Ordering molecular diversity in untargeted metabolomics via molecular community networking
Coler, E. A.; Melnik, A.; Lotfi, A.; Moradi, D.; Ahiadu, B.; Portal Gomes, P. W.; Patan, A.; Dorrestein, P. C.; Barnes, S.; Boginski, V.; Semenov, A.; Aksenov, A. A.
Show abstract
Natures molecular diversity is not random but displays intricate organization stemming from biological necessity. Molecular networking connects metabolites with structural similarity, enabling molecular discoveries from mass spectrometry data using arbitrary similarity thresholds that can fracture natural metabolite families. We present molecular community networking (MCN), that optimizes connectivity for each metabolite, rescuing lost relationships and capturing otherwise "hidden" metabolite connections. Using MCN, we demonstrate the discovery of novel dipeptide-conjugated bile acids.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- TidyMass2: Advancing LC-MS Untargeted Metabolomics Through Metabolite Origin Inference and Metabolic Feature-based Functional Module Analysis 96%
- Sequence-to-sequence translation from mass spectra to peptides with a transformer model 95%
- Enantioselective Protein Affinity Selection Mass Spectrometry (E-ASMS) 94%
Similar papers in this journal
- Polymorphic α-Glucans as Structural Scaffolds in Cryptococcus Cell Walls for Chitin, Capsule, and Melanin: Insights from 13C and 1H Solid-State NMR 92%
- Designing new natural-mimetic phosphatidic acid: aversatile and innovative synthetic strategy forglycerophospholipid research 91%
- LASSO: versatile and selective biomolecule pulldown with combinatorial DNA-crosslinked polymers 91%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.