Federated unsupervised random forest for privacy-preserving patient stratification
This work addresses privacy-preserving patient stratification in precision medicine, offering an incremental improvement through federated computing and enhanced interpretability.
The paper tackled patient stratification and disease subtyping using multi-omics data by developing a federated unsupervised random forest method, which improved cluster interpretability and showed competitive performance with state-of-the-art approaches on benchmark and cancer datasets.
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.