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Scalable prediction of acute myeloid leukemia using high-dimensional machine learning and blood transcriptomics.

Warnat-Herresthal, Stefanie and Perrakis, Konstantinos and Taschler, Bernd and Becker, Matthias and Baßler, Kevin and Beyer, Marc and Günther, Patrick and Schulte-Schrepping, Jonas and Seep, Lea and Klee, Kathrin and Ulas, Thomas and Haferlach, Torsten and Mukherjee, Sach and Schultze, Joachim L. (2020) 'Scalable prediction of acute myeloid leukemia using high-dimensional machine learning and blood transcriptomics.', iScience., 23 (1). p. 100780.

Abstract

Acute 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.

Item Type:Article
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Available under License - Creative Commons Attribution Non-commercial No Derivatives.
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1016/j.isci.2019.100780
Publisher statement:© 2020 The Authors.This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Date accepted:12 December 2019
Date deposited:18 June 2020
Date of first online publication:18 December 2019
Date first made open access:18 June 2020

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