Asymmetric generalisation and robustness of machine learning architectures in complex biological systems: The case of pancreatic ductal adenocarcinoma

Mikhail I. Krivonosov, Nina Avdeenko, Konstantin V. Ivashkin, Oleg Blyuss and Daniel Munblit

Chaos, Solitons & Fractals, 2026, vol. 209, issue P2

Abstract: Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, largely due to late diagnosis. Reliable early detection requires diagnostic models that account for biological heterogeneity, including established sex dimorphism in PDAC pathogenesis. This study investigates the structural stability and functional asymmetry of diagnostic models based on a five-predictor urinary biomarker panel (LYVE1, REG1B, TFF1, Age, Creatinine) and assesses diagnostic performance, algorithmic bias, and cross-sex generalisability across asymptomatic (PDAC vs Healthy) and symptomatic (PDAC vs Benign) clinical settings.

Keywords: Pancreatic ductal adenocarcinoma; Sex dimorphism; Algorithmic bias; Machine learning; Complex biological systems; Urinary biomarkers (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:209:y:2026:i:p2:s0960077926006909

DOI: 10.1016/j.chaos.2026.118549

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