Medizinische Universität Graz - Research portal
Selected Publication:
SHR Neuro Cancer Cardio Lipid Metab Microb
Moser, T; Kühberger, S; Lazzeri, I; Vlachos, G; Heitzer, E.
Bridging biological cfDNA features and machine learning approaches.
Trends Genet. 2023; 39(4): 285-307.
Doi: 10.1016/j.tig.2023.01.004
Web of Science
PubMed
FullText
FullText_MUG
- Leading authors Med Uni Graz
- Heitzer Ellen
- Kühberger Stefan
- Lazzeri Isaac
- Moser Tina
- Co-authors Med Uni Graz
- Vlachos Georgios
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- Abstract:
- Liquid biopsies (LBs), particularly using circulating tumor DNA (ctDNA), are expected to revolutionize precision oncology and blood-based cancer screening. Recent technological improvements, in combination with the ever-growing understanding of cell-free DNA (cfDNA) biology, are enabling the detection of tumor-specific changes with extremely high resolution and new analysis concepts beyond genetic alterations, including methylomics, fragmentomics, and nucleosomics. The interrogation of a large number of markers and the high complexity of data render traditional correlation methods insufficient. In this regard, machine learning (ML) algorithms are increasingly being used to decipher disease- and tissue-specific signals from cfDNA. Here, we review recent insights into biological ctDNA features and how these are incorporated into sophisticated ML applications.