Swarm Learning to identify COVID-19, tuberculosis and leukemia patients based on blood transcriptomes

Type: Transcriptome Analysis
Archiver: European Genome-Phenome Archive (EGA)

1 Dataset 2 Publications

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Dataset ID	Description	Technology	Samples
EGAD00001006231	Identification of patients with life-threatening diseases including leukemias or infections such as tuberculosis or COVID-19 is an important goal of modern precision medicine. However, there is an increasing divide between what is technically possible and what is allowed because of privacy legislation. We have recently illustrated that classical machine learning can identify leukemia patients based on their blood transcriptomes. To facilitate integration of any omics data from any data owner world-wide without violating privacy laws, we here introduce Swarm Learning (SL), a decentralized machine learning approach uniting edge computing, artificial intelligence (AI), blockchain and privacy protection without the need for a central coordinator thereby going beyond federated learning. To illustrate its feasibility, using more than 12,000 transcriptomes from peripheral blood mononuclear cells and more than 2,000 peripheral blood transcriptomes we demonstrate that SL of omics data distributed across different individual sites leads to disease classifiers that outperform those developed at individual sites. Yet, SL completely protects local privacy regulations by design. We propose this approach to noticeably accelerate the introduction of precision medicine.	NextSeq 500	650

Publications	Citations
Swarm Learning for decentralized and confidential clinical machine learning. Warnat-Herresthal S, Schultze H, Shastry KL, Manamohan S, Mukherjee S, Garg V, Sarveswara R, Händler K, Pickkers P, Aziz NA, Ktena S, Tran F, Bitzer M, Ossowski S, Casadei N, Herr C, Petersheim D, Behrends U, Kern F, Fehlmann T, Schommers P, Lehmann C, Augustin M, Rybniker J, Altmüller J, Mishra N, Mishra N, Bernardes JP, Krämer B, Bonaguro L, Schulte-Schrepping J, De Domenico E, Siever C, Kraut M, Desai M, Monnet B, Saridaki M, Siegel CM, Drews A, Nuesch-Germano M, Theis H, Heyckendorf J, Schreiber S, Kim-Hellmuth S, COVID-19 Aachen Study (COVAS), Nattermann J, Skowasch D, Kurth I, Keller A, Bals R, Nürnberg P, Rieß O, Rosenstiel P, Netea MG, Theis F, Mukherjee S, Backes M, Aschenbrenner AC, Ulas T, Deutsche COVID-19 Omics Initiative (DeCOI), Breteler MMB, Giamarellos-Bourboulis EJ, Kox M, Becker M, Cheran S, Woodacre MS, Goh EL, Schultze JL. Nature 594: 2021 265-270	148
Mature neutrophils and a NF-κB-to-IFN transition determine the unifying disease recovery dynamics in COVID-19. Frishberg A, Kooistra E, Nuesch-Germano M, Pecht T, Milman N, Reusch N, Warnat-Herresthal S, Bruse N, Händler K, Theis H, Kraut M, van Rijssen E, van Cranenbroek B, Koenen HJ, Heesakkers H, van den Boogaard M, Zegers M, Pickkers P, Becker M, Aschenbrenner AC, Ulas T, Theis FJ, Shen-Orr SS, Schultze JL, Kox M. Cell Rep Med 3: 2022 100652	8

Publications

Citations

Swarm Learning for decentralized and confidential clinical machine learning.
Warnat-Herresthal S, Schultze H, Shastry KL, Manamohan S, Mukherjee S, Garg V, Sarveswara R, Händler K, Pickkers P, Aziz NA, Ktena S, Tran F, Bitzer M, Ossowski S, Casadei N, Herr C, Petersheim D, Behrends U, Kern F, Fehlmann T, Schommers P, Lehmann C, Augustin M, Rybniker J, Altmüller J, Mishra N, Mishra N, Bernardes JP, Krämer B, Bonaguro L, Schulte-Schrepping J, De Domenico E, Siever C, Kraut M, Desai M, Monnet B, Saridaki M, Siegel CM, Drews A, Nuesch-Germano M, Theis H, Heyckendorf J, Schreiber S, Kim-Hellmuth S, COVID-19 Aachen Study (COVAS), Nattermann J, Skowasch D, Kurth I, Keller A, Bals R, Nürnberg P, Rieß O, Rosenstiel P, Netea MG, Theis F, Mukherjee S, Backes M, Aschenbrenner AC, Ulas T, Deutsche COVID-19 Omics Initiative (DeCOI), Breteler MMB, Giamarellos-Bourboulis EJ, Kox M, Becker M, Cheran S, Woodacre MS, Goh EL, Schultze JL. Nature 594: 2021 265-270

148

Mature neutrophils and a NF-κB-to-IFN transition determine the unifying disease recovery dynamics in COVID-19.
Frishberg A, Kooistra E, Nuesch-Germano M, Pecht T, Milman N, Reusch N, Warnat-Herresthal S, Bruse N, Händler K, Theis H, Kraut M, van Rijssen E, van Cranenbroek B, Koenen HJ, Heesakkers H, van den Boogaard M, Zegers M, Pickkers P, Becker M, Aschenbrenner AC, Ulas T, Theis FJ, Shen-Orr SS, Schultze JL, Kox M. Cell Rep Med 3: 2022 100652