Preprint
en Inglés
| PREPRINT-BIORXIV | ID: ppbiorxiv-171009
Identification of patients with life-threatening diseases including leukemias or infections such as tuberculosis and COVID-19 is an important goal of precision medicine. We recently illustrated that leukemiapatients are identified by machine learning (ML) based on their bloodtranscriptomes. However, there is an increasing divide between what is technically possible and what is allowed because of privacy legislation. To facilitate integration of any omics data from any data owner world-wide without violating privacylaws, we here introduce Swarm Learning (SL), a decentralized machine learning approach uniting edge computing, blockchain-based peer-to-peer networking and coordination as well as privacyprotection without the need for a central coordinator thereby going beyond federated learning. Using more than 14,000 bloodtranscriptomes derived from over 100 individual studies with non-uniform distribution of cases and controls and significant study biases, we illustrate the feasibility of SL to develop disease classifiers based on distributed data for COVID-19, tuberculosis or leukemias that outperform those developed at individual sites. Still, SL completely protects local privacyregulations by design. We propose this approach to noticeably accelerate the introduction of precision medicine.