In recent years the advent of multi-omic techniques have shown great promise in the field of oncology. In light of these advancements, this thesis focuses on the use of multiple data types to find methylation markers around transcription start site regions for colorectal cancer in the cell-free DNA (cfDNA) domain. It combines several methods of finding these markers, based on publicly available data obtained from solid tissue biopsies. These methods are both based on a single data type, as well as integrating multiple different data types. The resulting selections of methylation markers are then tested for significance on two independent datasets of cfDNA samples. The selections produced are tested on these datasets for their significance in distinguishing colorectal cancer samples from healthy samples. On one of these datasets, the selections are also tested for being differentially methylated between a group of patients with recurring tumors versus non-recurring tumors. The results on these two different datasets vary, showing that the methods of selecting potential methylation markers are capable of doing so on one platform, but that these results cannot be validated on another.

Due to the dramatic increase in enrollments in the TU Delft Bachelor of Computer
Science, the workload for teaching assistants and instructors has skyrocketed. To
reduce this workload, automated tools can be used to make the grading process easier. This paper describes the development of AuTA (Automatic Teaching Assistant), a tool that will help instructors and teaching assistants analyze and grade programming assignments and provide useful feedback to the student.