Machine learning to objectivate quality of Next-Generation Sequencing (NGS) samples

  • Category: ERASME
  • Swimlane: 2021-2022
  • Column: Done
  • Position: 11
  • Assignee: Christine Decaestecker
  • Creator: Christine Decaestecker
  • Started:
  • Created: 13/08/2018 10:52
  • Modified: 09/08/2022 10:11
  • Moved: 09/08/2022 10:11
Description

The aim of this Master thesis is to develop data analysis and machine learning methods able to establish DNA sample quality from a genomic dataset obtained by NGS. The NGS technology optimizes the molecular characterization of tumors which is essential to determine the most appropriate treatment for each patient ("personalized medicine"). The determination of the quality of the analyzed samples, sometimes very small (biopsies), is very important for the proper use of the information provided by NGS. This work will be done in close collaboration with the Molecular Biology unit of the Pathology Department of the ULB Erasme hospital.

Prerequisite: Basics of machine learning/data classification, programming (python or R)

Contact: Christine Decaestecker cdecaes(-at-)ulb.ac.be

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