Skip to content. | Skip to navigation

Personal tools
You are here: Home Theses Classification of Cancer with methylation aware motifs


Prof. Dr. S. Decker
RWTH Aachen
Informatik 5
Ahornstr. 55
D-52056 Aachen
Tel +49/241/8021501
Fax +49/241/8022321

How to find us

Annual Reports





Classification of Cancer with methylation aware motifs

Thesis type
  • Bachelor
Student Julia Gehrmann
Status Finished
Submitted in 2019
Proposal on 01. Apr 2019 00:00
Proposal room Seminarraum I5
Add proposal to calendar vCal

This dissertation addresses the problem of classification of cancer patients from DNA methylation. Mrs. Gehrman explores here the use of scores of transcription factors binding sites around DNA methylation as surrogate markers for DNA methylation. An innovative aspect is the fact binding site motifs take into consideration of the DNA methylation status of a given locus. Next, this work compared the performance of machine learning classifiers either using classical DNA methylation levels vs. TF binding affinity scores. For this, several classical machine learning methods (SVM, inductive trees, random forests) were used. Performance accuracy was similar with both data representations, however the computational time of training classifiers with TF binding site affinity scores were at least 10 times fasters, due to the lower dimensionality of the new space. This works supports promising features of DNA methylation aware TF binding scores.

Document Actions