In this study, we investigate the individuality and information content of infrared molecular profiles derived from blood samples in a large, longitudinal health-profiling cohort and compare them to a standard clinical laboratory panel. Using Fourier-transform infrared spectroscopy, we obtained comprehensive molecular fingerprints from 4,704 self-reported healthy individuals over five visits...
Early detection of lethal diseases such as lung cancer requires resolving faint signals amid biological heterogeneity. Precision screening aims to sensitively detect meaningful departures from an individual’s baseline by considering individual-level rather than population-level variability. This work investigates whether infrared molecular fingerprinting (IMF) - mid-infrared vibrational...
Personalized medicine aims to improve the treatment of complex diseases by tailoring therapies to the individual molecular characteristics of patients. This is possible by using multi-omics data, which combine different molecular modalities from the same individuals. Integrating these modalities allows more comprehensive and powerful modeling. However, their unique characteristics make...
Functional data analysis (FDA) has become increasingly popular in medical biometry and statistics. It is often appropriate to model observations by smooth curves or functions for example in the situation of observations that are sampled quite dense over time or space or in case of high-dimensional repeated measurements as FDA methods allow a flexible modelling. Furthermore they does not assume...
Large and high-dimensional biomedical datasets (large n and p) such as genotype data containing hundreds of thousands of genetic variants (SNPs) measured across many individuals require scalable algorithms to enable efficient model training. In this work, we address this challenge by leveraging principles of optimal design and informative subsampling.
We investigate the applicability of...
