Jove
Visualize
Contact Us

Related Concept Videos

Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved DNA...
Methods of Classification and Identification01:28

Methods of Classification and Identification

Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
Applications of Molecular Taxonomy01:20

Applications of Molecular Taxonomy

Molecular taxonomy has revolutionized the understanding and classification of bacteria, providing precise insights into their diversity, evolutionary relationships, and ecological roles. By utilizing molecular techniques such as DNA sequencing and fingerprinting, researchers have made significant strides in various fields related to bacterial studies.Resolving Taxonomic AmbiguitiesMolecular taxonomy has been instrumental in distinguishing closely related bacterial species initially thought to...

You might also read

Related Articles

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

Sort by
Same author

Accuracy of machine learning-assisted prediction of the future need for orthognathic surgery in patients with cleft lip and palate.

Korean journal of orthodontics·2025
Same author

COOBoostR: An Extreme Gradient Boosting-Based Tool for Robust Tissue or Cell-of-Origin Prediction of Tumors.

Life (Basel, Switzerland)·2023
Same author

Somatic mutation landscape reveals differential variability of cell-of-origin for primary liver cancer.

Heliyon·2020
Same author

Analysis of brain connectivity during nitrous oxide sedation using graph theory.

Scientific reports·2020
Same author

A pilot study using machine learning methods about factors influencing prognosis of dental implants.

The journal of advanced prosthodontics·2018
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 Experiment Video

Updated: Jul 5, 2026

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images
09:21

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

Published on: February 18, 2015

12.6K

Biologically-informed regional subset analysis with CatBoost for robust tissue-of-origin prediction.

Sungmin Yang1, Hong-Gee Kim1,2

  • 1Biomedical Knowledge Engineering Laboratory, Seoul National University School of Dentistry, Seoul, Republic of Korea.

Plos One
|December 4, 2025
PubMed
Summary

This study introduces a new genomic method for identifying cancer tissue of origin (TOO/COO). It efficiently pinpoints cancer types using fewer genomic regions, improving accuracy and reducing computational needs for precision oncology.

More Related Videos

Robust Comparison of Protein Levels Across Tissues and Throughout Development Using Standardized Quantitative Western Blotting
08:13

Robust Comparison of Protein Levels Across Tissues and Throughout Development Using Standardized Quantitative Western Blotting

Published on: April 9, 2019

14.8K
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

7.3K

Related Experiment Videos

Last Updated: Jul 5, 2026

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images
09:21

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

Published on: February 18, 2015

12.6K
Robust Comparison of Protein Levels Across Tissues and Throughout Development Using Standardized Quantitative Western Blotting
08:13

Robust Comparison of Protein Levels Across Tissues and Throughout Development Using Standardized Quantitative Western Blotting

Published on: April 9, 2019

14.8K
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

7.3K

Area of Science:

  • Genomics
  • Computational Biology
  • Oncology

Background:

  • Accurate cancer tissue/cell of origin (TOO/COO) identification is crucial for effective diagnosis and treatment.
  • Existing whole-genome analysis methods are computationally intensive and can be hindered by sparse mutation data.

Purpose of the Study:

  • To develop a computationally efficient and accurate method for determining cancer TOO/COO.
  • To reduce the computational burden of whole-genome analysis for cancer subtyping.

Main Methods:

  • Implemented an informative regional subset framework selecting significant 1Mbp genomic intervals.
  • Utilized a CatBoost prediction model trained on selected genomic regions.
  • Validated the approach on benchmark datasets (137 samples, 6 cancer types) and a larger cohort (934 PCAWG samples, 14 cancer lineages).

Main Results:

  • Achieved improved accuracy in melanoma (92.0%) and multiple myeloma (87.0%) compared to whole-genome analysis.
  • Reached perfect (100%) accuracy in high-mutation cancers like esophageal adenocarcinoma and glioblastoma with minimal regions.
  • Demonstrated scalability and matched or exceeded whole-genome performance across 14 cancer lineages.

Conclusions:

  • The regional subset framework offers a robust, scalable, and computationally efficient tool for cancer TOO/COO identification.
  • This approach enhances interpretability and supports precision oncology and diagnosis of cancers of unknown primary.
  • The method provides a generalizable solution for cancer subtyping and diagnosis.