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

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

8.5K
In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
8.5K
Replicative Cell Senescence02:15

Replicative Cell Senescence

3.6K
Replicative cell senescence is a property of cells that allows them to divide a finite number of times throughout the organism's lifespan while preventing excessive proliferation. Replicative senescence is associated with the gradual loss of the telomere — short, repetitive DNA sequences found at the end of the chromosomes. Telomeres are bound by a group of proteins to form a protective cap on the ends of chromosomes. Embryonic stem cells express telomerase — an enzyme that adds...
3.6K
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. A Novel Machine Learning Approach For Tumor Detection Based On Telomeric Signatures.
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. A Novel Machine Learning Approach For Tumor Detection Based On Telomeric Signatures.

Related Experiment Video

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K

A NOVEL MACHINE LEARNING APPROACH FOR TUMOR DETECTION BASED ON TELOMERIC SIGNATURES.

Priyanshi Shah1, Arun Sethuraman1

  • 1Department of Biology, San Diego State University, San Diego, California, 92182, United States of America.

Biorxiv : the Preprint Server for Biology
|June 12, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces TeloQuest, a machine learning model using telomere length and genomic data to predict cancer status with 82.62% accuracy. This tool aids in cancer diagnostics and risk assessment.

More Related Videos

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors
06:32

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Published on: August 18, 2023

1.9K
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

8.6K

Related Experiment Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K
Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors
06:32

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Published on: August 18, 2023

1.9K
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

8.6K

Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Cancer encompasses over 200 types, each with unique molecular profiles requiring tailored therapies.
  • Both short and long telomere lengths are linked to increased cancer risk, indicating telomere length variation's role in tumorigenesis.

Purpose of the Study:

  • To develop and validate a machine learning model for predicting tumor status using telomere characteristics.
  • To integrate telomere biology with large-scale genomic and phenotypic data for enhanced cancer diagnostics.

Main Methods:

  • A supervised machine learning model was developed.
  • The model was trained on telomeric read content, genomic variants, and phenotypic features.
  • Data from 33 cancer types within The Cancer Genome Atlas (TCGA) program were utilized.

Main Results:

  • The developed model achieved an 82.62% accuracy in predicting tumor status.
  • The study highlights telomere length variation as a potential predictive biomarker in oncology.

Conclusions:

  • The TeloQuest model offers a novel, multidisciplinary approach to cancer diagnostics and risk assessment.
  • Integrating telomere biology with genomic and phenotypic data shows promise for improving oncological outcomes.
  • The trained model is publicly available for further research and development.