Quantifying complementarity between different cfDNA features

Detection of cancer using blood

Bachelor thesis (2024)

Authors

A. Farooq Electrical Engineering, Mathematics and Computer Science

Contributors

M. J.T. Reinders Pattern Recognition and Bioinformatics - EEMCS (mentor)
Stavros Makrodimitris Pattern Recognition and Bioinformatics - EEMCS (mentor)
I.B. Pronk Pattern Recognition and Bioinformatics - EEMCS (mentor)
D.M. Hazelaar Erasmus MC (mentor)
Johan Pouwelse Data-Intensive Systems - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
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Published Date

28-06-2024

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Recent research has indicated attributes of cell-free DNA (cfDNA) called fragmentomics
as a promising method for late stage cancer detection in a non-invasive manner. The pri-
mary objective of this research is to uncover hidden patterns and interactions that could
enhance the accuracy and sensitivity of blood-based cancer diagnostics. This study explores
the complementarity between three fragmentomics features; fragment length distribution,
and nucleotide fragment end sequence diversity and nucleosome positioning for four dif-
ferent sample groups; breast cancer, colorectal cancer, lung cancer and healthy controls.
Various machine learning techniques such as linear regression were employed to quantify
any complementary relationships between the features

Files

Research_Paper_CSE3000_AMR.pdf
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