Comparing machine learning models for predicting mutation status in Acute Myeloid Leukemia patients using RNA-seq data

Abstract Acute Myeloid Leukemia (AML) is a highly heterogeneous disease. The current AML classifications are based mainly on molecular markers, including cytogenetics features, fusion genes, and the presence or absence of mutations. In this study, we investigated mutation status in AML patients through RNA-seq data in link with differential gene expression. We applied seven machine learning algorithms to identify the presence or absence of NPM1, IDH1/IDH2, and FLT3-ITD mutations, reaching 95%, 93%, and 87% accuracy, respectively. In each case, the best performing models were complex models, suggesting highly complex biological processes at work behind AML. Competing Interest Statement The authors have declared no competing interest. Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.