ArticleArXiv2026
The Language of Elution: Autoregressive Prediction of the Next Feature in Untargeted LC-HRMS Lipidomics.
Article in ArXiv, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
1 author.
Funding
Abstract
Untargeted liquid chromatography-high-resolution mass spectrometry (LC-HRMS) routinely detects thousands of molecular features per sample, yet only 2-20% receive confident structural annotations. A root cause of this "dark metabolome" is that tandem mass spectrometry (MS/MS) acquisition remains reactive: instruments select precursor ions after they appear, with no foreknowledge of what will elute next. Here we reframe chromatographic elution as an autoregressive sequence prediction task. Because reversed-phase elution order is governed by hydrophobicity, successive features are not independent draws but elements of a physically constrained sequence-analogous to tokens in natural language. We discretize the mass-to-charge (
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Identifiers
42282048PMC13252500What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.