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Conditional Deep Generative Modeling of Blood-Based Infrared Spectra Enables Controlled In-Silico Phenotyping Studies

MCML Authors

Gitta Kutyniok

Prof. Dr.

Principal Investigator

Mathematical Foundations of Artificial Intelligence

Abstract

Infrared molecular fingerprinting of blood offers a scalable, minimally invasive window into human physiology, but limited follow-up, imbalanced cohorts, and restricted access to diverse phenotypes constrain systematic studies. Here, we introduce a conditional deep generative framework for synthesizing blood-based infrared spectra that preserves individual-level structure while allowing controlled manipulation of demographic and anthropometric covariates. Using 25,308 spectra from 5,863 ostensibly healthy participants in the longitudinal Health4Hungary – Hungary4Health cohort, we train a Conditional Variational Autoencoder and a Conditional Boundary Equilibrium GAN to generate blood-based infrared spectra conditioned on age, sex, and body mass index. We show that the generated spectra closely match held-out real data across multiple levels and faithfully encode demographic and anthropometric information. We further demonstrate two in-silico applications: modeling of individualized healthy aging trajectories that follow cohort-level aging manifolds while retaining subject-specific characteristics, and targeted augmentation of underrepresented body mass index categories. Together, these results establish conditional generative modeling of blood-based infrared spectra as a viable approach for virtual cohort construction, cohort balancing, and controlled in-silico phenotyping, paving the way toward more comprehensive and data-efficient studies in precision health.

misc LHS+26

Preprint

Feb. 2026

Authors

N. Leopold-Kerschbaumer • T. Halenke • S. Süzeroğlu • M. Jung • M. Seleznova • N. Thorisch • J. Lorenz • T. Bocklitz • B. M. Eskofier • G. Kutyniok • F. Krausz • K.