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Towards rapid prediction of drug-resistant cancer cell phenotypes: single cell mass spectrometry combined with machine learning

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Abstract

Combined single cell mass spectrometry and machine learning methods is demonstrated for the first time to achieve rapid and reliable prediction of the phenotype of unknown single cells based on their metabolomic profiles, with experimental validation. This approach can be potentially applied towards prediction of drug-resistant phenotypes prior to chemotherapy.

Graphical abstract: Towards rapid prediction of drug-resistant cancer cell phenotypes: single cell mass spectrometry combined with machine learning

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Publication details

The article was received on 17 Oct 2018, accepted on 29 Nov 2018 and first published on 29 Nov 2018


Article type: Communication
DOI: 10.1039/C8CC08296K
Citation: Chem. Commun., 2019, Advance Article
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    Towards rapid prediction of drug-resistant cancer cell phenotypes: single cell mass spectrometry combined with machine learning

    R. Liu, G. Zhang and Z. Yang, Chem. Commun., 2019, Advance Article , DOI: 10.1039/C8CC08296K

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