Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance

Abstract excerpt

Resistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learning classifiers. Despite the acknowledged role of tumor metabolism in radiation response, metabolomics data is rarely collected in large multi-omics initiatives such as...

Topics

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• Bioinformatics and Genomic Networks
• Cancer Genomics and Diagnostics
• Cancer, Hypoxia, and Metabolism
• Epigenetics and DNA Methylation
• Glioma Diagnosis and Treatment
• Metabolomics and Mass Spectrometry Studies
• Pancreatic and Hepatic Oncology Research
• Radiomics and Machine Learning in Medical Imaging

Identifiers and source

Literature Corpus work

ea36436b-fce7-5482-88f1-39e21dc8c3c5

DOI

10.1101/2020.08.02.233098