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Original Articles

Machine learning derived genomics driven prognostication for acute myeloid leukemia with RUNX1-RUNX1T1

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Pages 3154-3160 | Received 15 Jun 2020, Accepted 15 Jul 2020, Published online: 05 Aug 2020

Figures & data

Table 1. Prognostic significance of machine learning derived genetic risk in AML with t(8;21).

Figure 1. The above circos plot (A) highlights the spectrum of mutations and their interaction in AML with RUNX1-RUNX1T1. Commonly occurring gene mutations are colored. The machine learning derived scoring system is described in (B). The Kaplan–Meier plot in the top right section (C) shows the clinical impact on overall survival (OS) and for relapse free survival (RFS, D), lower right).

Figure 1. The above circos plot (A) highlights the spectrum of mutations and their interaction in AML with RUNX1-RUNX1T1. Commonly occurring gene mutations are colored. The machine learning derived scoring system is described in (B). The Kaplan–Meier plot in the top right section (C) shows the clinical impact on overall survival (OS) and for relapse free survival (RFS, D), lower right).
Supplemental material

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