Finding biological markers for the prediction of colorectal cancer

Sloof, A.J.G.

Finding biological markers for the prediction of colorectal cancer

Using machine learning methods to identify functional biomarkers in the human gut microbiome

Bachelor thesis (2023)

Authors

A.J.G. Sloof Electrical Engineering, Mathematics and Computer Science

Contributors

Thomas Abeel Pattern Recognition and Bioinformatics (mentor)

E.A. van der Toorn Pattern Recognition and Bioinformatics (mentor)

D. Calderon Franco (mentor)

Thomas Hollt Computer Graphics and Visualisation (graduation committee member)

Faculty

Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science

Neural Network Colorectal Cancer Biomarkers Metagenomics

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Published Date

29-06-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

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.

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