Ontology highlight
ABSTRACT:
SUBMITTER: Warnat-Herresthal S
PROVIDER: S-EPMC8189907 | biostudies-literature | 2021 Jun
REPOSITORIES: biostudies-literature
Warnat-Herresthal Stefanie S Schultze Hartmut H Shastry Krishnaprasad Lingadahalli KL Manamohan Sathyanarayanan S Mukherjee Saikat S Garg Vishesh V Sarveswara Ravi R Händler Kristian K Pickkers Peter P Aziz N Ahmad NA Ktena Sofia S Tran Florian F Bitzer Michael M Ossowski Stephan S Casadei Nicolas N Herr Christian C Petersheim Daniel D Behrends Uta U Kern Fabian F Fehlmann Tobias T Schommers Philipp P Lehmann Clara C Augustin Max M Rybniker Jan J Altmüller Janine J Mishra Neha N Mishra Neha N Bernardes Joana P JP Krämer Benjamin B Bonaguro Lorenzo L Schulte-Schrepping Jonas J De Domenico Elena E Siever Christian C Kraut Michael M Desai Milind M Monnet Bruno B Saridaki Maria M Siegel Charles Martin CM Drews Anna A Nuesch-Germano Melanie M Theis Heidi H Heyckendorf Jan J Schreiber Stefan S Kim-Hellmuth Sarah S Nattermann Jacob J Skowasch Dirk D Kurth Ingo I Keller Andreas A Bals Robert R Nürnberg Peter P Rieß Olaf O Rosenstiel Philip P Netea Mihai G MG Theis Fabian F Mukherjee Sach S Backes Michael M Aschenbrenner Anna C AC Ulas Thomas T Breteler Monique M B MMB Giamarellos-Bourboulis Evangelos J EJ Kox Matthijs M Becker Matthias M Cheran Sorin S Woodacre Michael S MS Goh Eng Lim EL Schultze Joachim L JL
Nature 20210526 7862
Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine<sup>1,2</sup>. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes<sup>3</sup>. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation<sup>4,5</sup>. Here, to facilitate the integration of any medical data from any data owner worldwide without violating ...[more]