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)
Author(s)

A.J.G. Sloof (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

Thomas Abeel – Mentor (TU Delft - Pattern Recognition and Bioinformatics)

E.A. van der Toorn – Mentor (TU Delft - Pattern Recognition and Bioinformatics)

David Calderon Franco – Mentor

Thomas Hollt – Graduation committee member (TU Delft - Computer Graphics and Visualisation)

Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright
© 2023 Jos Sloof
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 Jos Sloof
Graduation Date
29-06-2023
Awarding Institution
Delft University of Technology
Project
CSE3000 Research Project
Programme
Computer Science and Engineering
Faculty
Electrical Engineering, Mathematics and Computer Science
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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.

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