Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

6.0K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
6.0K
Genetic Screens02:46

Genetic Screens

5.0K
Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
5.0K
Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

69
Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
69

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A comprehensive urine workflow enables robust methylation and fragmentation analysis of cell-free DNA.

Scientific reports·2026
Same author

Medizinische Genetik : Mitteilungsblatt des Berufsverbandes Medizinische Genetik e.V·2026
Same author

Long-term prognostic value of ctDNA in early breast cancer: insights from the neoadjuvant ABCSG-34 Trial.

NPJ breast cancer·2026
Same author

Reply to Dr Kemal Örnek Regarding Comments on: "Compound Heterozygosity for the C6777T Mutation of the MTHFR Gene and the FII G20210A Mutation of the Prothrombin Gene in Sequential Bilateral Anterior Ischemic Optic Neuropathy".

Neuro-ophthalmology (Aeolus Press)·2026
Same author

Reference materials and external quality assessment for liquid biopsy assays: Expert opinions from the European Liquid Biopsy Society ctDNA workshop.

Cancer treatment and research communications·2026
Same author

Listening to the bladder field: Urine liquid biopsies reveal differential treatment responses in NMIBC.

Cell·2026

Related Experiment Video

Updated: Aug 9, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

98

Bridging biological cfDNA features and machine learning approaches.

Tina Moser1, Stefan Kühberger1, Isaac Lazzeri1

  • 1Institute of Human Genetics, Diagnostic & Research Center for Molecular BioMedicine, Medical University of Graz, Neue Stiftingtalstrasse 6, 8010 Graz, Austria; Christian Doppler Laboratory for Liquid Biopsies for Early Detection of Cancer, Medical University of Graz, Graz, Austria.

Trends in Genetics : TIG
|February 15, 2023
PubMed
Summary

Liquid biopsies using circulating tumor DNA (ctDNA) show promise for cancer screening. Machine learning algorithms are crucial for analyzing complex cfDNA data to detect cancer signals.

Keywords:
cell-free DNA (cfDNA)circulating tumor DNA (ctDNA)fragmentomicsmachine learning (ML)methylomicsnucleosomics

More Related Videos

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.4K
Detection and Monitoring of Tumor Associated Circulating DNA in Patient Biofluids
06:53

Detection and Monitoring of Tumor Associated Circulating DNA in Patient Biofluids

Published on: June 8, 2019

8.7K

Related Experiment Videos

Last Updated: Aug 9, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

98
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.4K
Detection and Monitoring of Tumor Associated Circulating DNA in Patient Biofluids
06:53

Detection and Monitoring of Tumor Associated Circulating DNA in Patient Biofluids

Published on: June 8, 2019

8.7K

Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Liquid biopsies (LBs) and circulating tumor DNA (ctDNA) are transforming precision oncology and cancer screening.
  • Advances in cell-free DNA (cfDNA) analysis enable high-resolution detection of tumor-specific alterations.
  • New analytical concepts extend beyond genetic changes to include methylomics, fragmentomics, and nucleosomics.

Purpose of the Study:

  • To review recent biological insights into ctDNA features.
  • To explore the application of machine learning (ML) in analyzing cfDNA data for cancer detection.
  • To highlight the role of ML in deciphering complex biological signals from cfDNA.

Main Methods:

  • Review of current literature on ctDNA biology and analysis.
  • Examination of machine learning algorithms applied to cfDNA data.
  • Discussion of novel cfDNA analysis concepts (methylomics, fragmentomics, nucleosomics).

Main Results:

  • Technological advancements facilitate high-resolution detection of ctDNA.
  • Complex cfDNA data necessitates sophisticated analytical approaches beyond traditional methods.
  • Machine learning algorithms are increasingly vital for interpreting disease- and tissue-specific signals in cfDNA.

Conclusions:

  • Liquid biopsies and ctDNA analysis hold significant potential for revolutionizing cancer care.
  • Integrating biological ctDNA features with advanced ML applications is key to unlocking their full potential.
  • Further research into ML-driven cfDNA analysis will enhance cancer screening and precision oncology.