This paper is in the following e-collection/theme issue:

Clinical Information and Decision Making (1407) Innovations and Technology in Cancer Care (423) Memory (43)

Published on in Vol 22, No 4 (2020): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/16533, first published .
Optimizing the Analytical Value of Oncology-Related Data Based on an In-Memory Analysis Layer: Development and Assessment of the Munich Online Comprehensive Cancer Analysis Platform

Optimizing the Analytical Value of Oncology-Related Data Based on an In-Memory Analysis Layer: Development and Assessment of the Munich Online Comprehensive Cancer Analysis Platform

Optimizing the Analytical Value of Oncology-Related Data Based on an In-Memory Analysis Layer: Development and Assessment of the Munich Online Comprehensive Cancer Analysis Platform

Authors of this article:

Daniel Nasseh1, 2 Author Orcid Image ;   Sophie Schneiderbauer1, 2 Author Orcid Image ;   Michael Lange1, 3 Author Orcid Image ;   Diana Schweizer2 Author Orcid Image ;   Volker Heinemann1, 2, 4 Author Orcid Image ;   Claus Belka1, 4, 5 Author Orcid Image ;   Ranko Cadenovic6 Author Orcid Image ;   Laurence Buysse6 Author Orcid Image ;   Nicole Erickson2 Author Orcid Image ;   Michael Mueller7 Author Orcid Image ;   Karsten Kortuem7 Author Orcid Image ;   Maximilian Niyazi4, 5 Author Orcid Image ;   Sebastian Marschner4, 5 Author Orcid Image ;   Theres Fey2 Author Orcid Image
Article Authors Cited by (4) Tweetations (2) Metrics

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  4. Kasprzak J, Westphalen C, Frey S, Schmitt Y, Heinemann V, Fey T, Nasseh D. Supporting the decision to perform molecular profiling for cancer patients based on routinely collected data through the use of machine learning. Clinical and Experimental Medicine 2024;24(1) View