Bridging biological cfDNA features and machine learning approaches.
cell-free DNA (cfDNA)
circulating tumor DNA (ctDNA)
fragmentomics
machine learning (ML)
methylomics
nucleosomics
Journal
Trends in genetics : TIG
ISSN: 0168-9525
Titre abrégé: Trends Genet
Pays: England
ID NLM: 8507085
Informations de publication
Date de publication:
04 2023
04 2023
Historique:
received:
29
06
2022
revised:
10
01
2023
accepted:
19
01
2023
pubmed:
16
2
2023
medline:
24
3
2023
entrez:
15
2
2023
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 36792446
pii: S0168-9525(23)00019-7
doi: 10.1016/j.tig.2023.01.004
pii:
doi:
Substances chimiques
Cell-Free Nucleic Acids
0
Circulating Tumor DNA
0
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
285-307Informations de copyright
Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of interests No interests are declared.