Finding biological markers for the prediction of colorectal cancer
Using machine learning methods to identify functional biomarkers in the human gut microbiome
A.J.G. Sloof (TU Delft - Electrical Engineering, Mathematics and Computer Science)
TEPMF Abeel – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
E.A. van der Toorn – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
David Calderon Franco – Mentor
T. Höllt – Graduation committee member (TU Delft - Computer Graphics and Visualisation)
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Abstract
Colorectal cancer (CRC), one of the leading causes of mortality, is challenging to diagnose. By using metagenomic analysis with machine learning methods, this can be done in a non-invasive manner. In this research, a neural network has been trained on relative pathway abundance data, a way to measure the functional potential of a microbiome, in order to find biomarkers for colorectal cancer. The accuracy achieved by the neural network is 57%. The most important features used by the model are compared to established biomarkers in literature. Besides overlapping pathways, this research also found new potential biomarkers for CRC.