Health AI Central
Peer-Reviewed Publication
ACS Omega2026;11(22):32946-32954.June 9, 2026Journal Article

Learning to See Peaks: Attention-Based Feature Extraction for Automated Chromatographic Peak Detection.

Daniel Walter1, Mathias Helbig1, Birgit Weydanz1, Dominik Voltmer1, Juan Jose Bonfiglio1, Carsten Marr2,3,4,5, Tobias Großkopf1
1Pharma Research and Early Development, Roche Diagnostics GmbH, Penzberg 82377 Germany.
2Department of Medicine III, Ludwig-Maximilian-University Hospital, Marchioninistr. 15, Munich 81377, Germany.
3Institute of AI for Health, Computational Health Center, Helmholtz Zentrum München, Ingolstädter Landstrasse 1, Neuherberg 85764, Germany.
4German Cancer Consortium (DKTK), partner site Munich, a partnership between DKFZ and LMU University Hospital Munich, Heidelberg 69120 Germany.
5Munich Center for Machine Learning (MCML), Munich 80538 Germany.
PubMed PMC DOI PDF

Abstract

Reliable peak detection remains a bottleneck in size-exclusion chromatography (SEC) as overlapping signals, drifting baselines, and analyst variability limit reproducibility. As SEC is a routine release and comparability assay and its interpretation depends on peak morphology and context, machine learning methods are well-suited to improve reproducibility at scale. We present the Peak Feature Extr…

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