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Eissa, T.* ; Huber, M.* ; Obermayer-Pietsch, B.* ; Linkohr, B. ; Peters, A. ; Fleischmann, F.* ; Zigman, M.*

CODI: Enhancing machine learning-based molecular profiling through contextual out-of-distribution integration.

PNAS Nexus 3:pgae449 (2024)
Verlagsversion DOI PMC
Free journal
Creative Commons Lizenzvertrag
Molecular analytics increasingly utilize machine learning (ML) for predictive modeling based on data acquired through molecular profiling technologies. However, developing robust models that accurately capture physiological phenotypes is challenged by the dynamics inherent to biological systems, variability stemming from analytical procedures, and the resource-intensive nature of obtaining sufficiently representative datasets. Here, we propose and evaluate a new method: Contextual Out-of-Distribution Integration (CODI). Based on experimental observations, CODI generates synthetic data that integrate unrepresented sources of variation encountered in real-world applications into a given molecular fingerprint dataset. By augmenting a dataset with out-of-distribution variance, CODI enables an ML model to better generalize to samples beyond the seed training data, reducing the need for extensive experimental data collection. Using three independent longitudinal clinical studies and a case-control study, we demonstrate CODI's application to several classification tasks involving vibrational spectroscopy of human blood. We showcase our approach's ability to enable personalized fingerprinting for multiyear longitudinal molecular monitoring and enhance the robustness of trained ML models for improved disease detection. Our comparative analyses reveal that incorporating CODI into the classification workflow consistently leads to increased robustness against data variability and improved predictive accuracy.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Data Augmentation ; Machine Learning ; Molecular Analytics ; Out-of-distribution ; Variability Modeling; Metabolic Phenotypes; Spectroscopy; Hallmarks; Cancer
ISSN (print) / ISBN 2752-6542
e-ISSN 2752-6542
Zeitschrift PNAS Nexus
Quellenangaben Band: 3, Heft: 10, Seiten: , Artikelnummer: pgae449 Supplement: ,
Verlag Oxford University Press
Verlagsort Great Clarendon St, Oxford Ox2 6dp, England
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Epidemiology (EPI)
Förderungen Styrian Business Promotion Agency (SFG)
Austrian Federal Ministry of Economics and Labour/the Federal Ministry of Economy, Family and Youth (BMWA/BMWFJ)
Austrian Research Fund, as a COMET K-project - Austrian Federal Ministry of Transport, Innovation and Technology (BMVIT)
State of Bavaria
Helmholtz Zentrum Munchen-German Research Center for Environmental Health - German Federal Ministry of Education and Research (BMBF)
LMU Munich, Centre for Advanced Laser Applications (CALA)