PeakCloud: Benchmarking and Learning on Peak-Level Mass Spectra for Metabolomic Pattern Recognition

Mass spectra acquired from non-invasive biospecimens such as urine, breath, and feces enable untargeted metabolomics to capture comprehensive molecular profiles for disease characterization. While machine learning can extract informative patterns from these spectra, current studies are largely confined to single cohorts and suffer from disparate data processing pipelines and evaluation protocols, limiting systematic assessments across multiple cohorts and data formats. To address these challenges, we establish PeakCloud, a benchmark covering nine classification tasks across three public datasets and an clinical cohort of 700 subjects. Crucially, this clinical cohort contains paired measurements across two instruments, enabling controlled evaluation of hardware shifts alongside cross-batch settings under unified protocols. Within this benchmark, although CNN-based deep learning models are competitive on several tasks, their reliance on dense spectral grids limits biological explainability. We therefore introduce PeakCloudNet, a set-based neural network that processes raw spectra while preserving individual peak identities for attribution. PeakCloudNet achieves competitive performance against grid-based models, accurately pinpoints disease-associated peaks, and retrieves candidate formulas aligned with known metabolic pathways. We hope that this benchmark and architecture will facilitate generalizable and interpretable pattern recognition in metabolomics. Upon publication, we will release the processed peak-level data for all datasets in PeakCloud, together with the PeakCloudNet implementation, to support reproducible research in this area.