Federated unsupervised random forest for privacy-preserving patient
  stratification
Bastian Pfeifer Christel Sirocchi Marcus D. Bloice Markus Kreuzthaler Martin Urschler
In the realm of precision medicine, effective patient stratification and
disease subtyping demand innovative methodologies tailored for multi-omics
data. Clustering techniques applied to multi-omics data have become
instrumental in identifying distinct subgroups of patients, enabling a
finer-grained understanding of disease variability. This work establishes a
powerful framework for advancing precision medicine through unsupervised
random-forest-based clustering and federated computing. We introduce a novel
multi-omics clustering approach utilizing unsupervised random-forests. The
unsupervised nature of the random forest enables the determination of
cluster-specific feature importance, unraveling key molecular contributors to
distinct patient groups. Moreover, our methodology is designed for federated
execution, a crucial aspect in the medical domain where privacy concerns are
paramount. We have validated our approach on machine learning benchmark data
sets as well as on cancer data from The Cancer Genome Atlas (TCGA). Our method
is competitive with the state-of-the-art in terms of disease subtyping, but at
the same time substantially improves the cluster interpretability. Experiments
indicate that local clustering performance can be improved through federated
computing.