A novel study from the University of South Australia has identified a range of metabolic biomarkers that could help predict the risk of cancer.
Deploying machine learning to examine data from 459,169 participants in the UK Biobank, the study identified 84 features that could signal increased cancer risk.
We conducted a hypothesis-free analysis using artificial intelligence and statistical approaches to identify cancer risk factors among more than 2800 features. More than 40% of the features identified by the model were found to be biomarkers – biological molecules that can signal health or unhealthy conditions depending on their status – and several of these were jointly linked to cancer risk and kidney or liver disease.
Related Stories“We also identified that greater red cell distribution width – or the variation in size of your red blood cells – is associated with increased risk of cancer. Chief investigator, Professor Elina Hyppönen, Centre Director of the Australian Centre for Precision Health at UniSA, says the strength of this study lies in the machine learning.
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