Related Experiment Video
Updated: Jun 1, 2025
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
Simplifying clinical use of TCGA molecular subtypes through machine learning models
Kevin M Boehm1, Francisco Sánchez-Vega2
1Computational Oncology Service, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
Abstract:
In this issue of Cancer Cell, Ellrott et al. present machine learning models to classify samples into The Cancer Genome Atlas molecular subtypes using compact sets of genomic features. These validated, ready-to-use models are publicly available, although some clinical hurdles need to be cleared before they are fully implemented.
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