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

Journal: Chemical communications (Cambridge, England)

Published Date: Jan 10, 2019

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.

Authors

Renmeng Liu

Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma 73019, USA. Zhibo.Yang@ou.edu.
Genwei Zhang

Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, OK, 73019, USA.
Zhibo Yang

Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, OK, 73019, USA. Electronic address: Zhibo.Yang@ou.edu.

Keywords

Drug Resistance, Neoplasm Humans K562 Cells Leukemia, Myelogenous, Chronic, BCR-ABL Positive Logistic Models Machine Learning Mass Spectrometry Metabolomics Neural Networks, Computer Phenotype Single-Cell Analysis Stochastic Processes

External Resources

View on PubMed Access via DOI PubMed (30525135)

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

Abstract

Authors

Keywords

External Resources

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