摘要：SummaryAcute myeloid leukemia (AML) is a severe, mostly fatal hematopoietic malignancy. We were interested in whether transcriptomic-based machine learning could predict AML status without requiring expert input. Using 12,029 samples from 105 different studies, we present a large-scale study of machine learning-based prediction of AML in which we address key questions relating to the combination of machine learning and transcriptomics and their practical use. We find data-driven, high-dimensional approaches—in which multivariate signatures are learned directly from genome-wide data with no prior knowledge—to be accurate and robust. Importantly, these approaches are highly scalable with low marginal cost, essentially matching human expert annotation in a near-automated workflow. Our results support the notion that transcriptomics combined with machine learning could be used as part of an integrated -omics approach wherein risk prediction, differential diagnosis, and subclassification of AML are achieved by genomics while diagnosis could be assisted by transcriptomic-based machine learning.Graphical AbstractDisplay OmittedHighlights•Study presents one of the largest transcriptomics datasets to date for AML prediction•Effective classifiers can be obtained by high-dimensional machine learning•Accuracy increases with dataset size•Includes challenging scenarios such as cross-study and cross-technologyArtificial Intelligence; Biological Sciences; Cancer; Computer Science; Omics; Transcriptomics
