A recent paper by the Data Science Research Group has been selected for the front cover of Analytical Methods, a journal of the Royal Society of Chemistry. In the related article, published by Leopold-Kerschbaumer et al. and titled “Synthetic blood-based infrared molecular fingerprints: artificial cohorts for methodological research,” the team introduces a new resource for advancing computational and analytical methods in biomedical spectroscopy.
Infrared molecular fingerprinting of human blood samples is a promising minimally invasive approach for disease detection and health monitoring. However, real clinical datasets are often difficult to share because they contain sensitive patient information and are subject to strict ethical and legal constraints. To address this challenge, Leopold-Kerschbaumer et al. developed synthetic blood-based infrared molecular fingerprints that preserve key statistical and physical characteristics of real measurements while avoiding the disclosure of patient data.
The synthetic cohorts were generated using multivariate Gaussian models fitted to real-world infrared spectra from a large case-control study covering several cancer types. The authors show that the artificial datasets closely reproduce important properties of the original data and can serve as realistic proxies for methodological research. This makes them valuable for developing, benchmarking, and validating statistical, machine-learning, and signal-processing approaches under reproducible conditions.
The front-cover selection highlights the relevance of this work for the broader analytical chemistry and biomedical spectroscopy communities. By making synthetic, privacy-preserving datasets available for open use, this work is helping to lower barriers to collaboration, improve reproducibility, and accelerate innovation in spectroscopic biomarker research.
Picture: Kosmas Kepesidis
Original publication:
synthetic blood-based infrared molecular fingerprints: artificial cohorts for methodological research
Niklas Leopold-Kerschbaumer, Nico Feiler & Kosmas V. Kepesidis
Analytical Methods 18 (2026)
DOI: 10.1039/d5ay02166a
