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

You might also read

Related Articles

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

Sort by
Same author

Machine Learning Identification of Cell-Type-Specific Molecular Signatures Distinguishing COVID-19 from Other Lower Respiratory Tract Diseases.

Life (Basel, Switzerland)·2026
Same author

Machine Learning-Based Identification of Candidate Serum miRNA Features for Pan-Cancer and Cancer Type Classification.

Life (Basel, Switzerland)·2026
Same author

Association between gut microbiota and imaging biomarkers in arteriosclerotic cerebral small vessel disease with idiopathic normal pressure hydrocephalus.

Frontiers in neuroscience·2026
Same author

Network Analysis in Microbiome Research: Methods, Tools, and Applications.

Methods in molecular biology (Clifton, N.J.)·2026
Same author

The Role of EBV Infection and Epigenetic Factors in Radioresistance of Colorectal Cancer.

Methods in molecular biology (Clifton, N.J.)·2026
Same author

RNA Large Language Models in Virology.

Methods in molecular biology (Clifton, N.J.)·2026

Related Experiment Video

Updated: Jul 15, 2025

Microfluidic Co-Culture Models for Dissecting the Immune Response in in vitro Tumor Microenvironments
07:46

Microfluidic Co-Culture Models for Dissecting the Immune Response in in vitro Tumor Microenvironments

Published on: April 30, 2021

4.8K

Identification of Colon Immune Cell Marker Genes Using Machine Learning Methods.

Yong Yang1, Yuhang Zhang2, Jingxin Ren3

  • 1Qianwei Hospital of Jilin Province, Changchun 130012, China.

Life (Basel, Switzerland)
|September 28, 2023
PubMed
Summary

Researchers identified key genetic markers for 25 immune cell types in the colon using machine learning. These markers help differentiate immune cells, aiding in understanding colon cancer microenvironments and improving immunotherapy.

Keywords:
colon immune cellfeature selectionmachine learningmarker gene

More Related Videos

Colorectal Cancer Cell Surface Protein Profiling Using an Antibody Microarray and Fluorescence Multiplexing
15:17

Colorectal Cancer Cell Surface Protein Profiling Using an Antibody Microarray and Fluorescence Multiplexing

Published on: September 25, 2011

14.0K
Predictive Immune Modeling of Solid Tumors
08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

7.0K

Related Experiment Videos

Last Updated: Jul 15, 2025

Microfluidic Co-Culture Models for Dissecting the Immune Response in in vitro Tumor Microenvironments
07:46

Microfluidic Co-Culture Models for Dissecting the Immune Response in in vitro Tumor Microenvironments

Published on: April 30, 2021

4.8K
Colorectal Cancer Cell Surface Protein Profiling Using an Antibody Microarray and Fluorescence Multiplexing
15:17

Colorectal Cancer Cell Surface Protein Profiling Using an Antibody Microarray and Fluorescence Multiplexing

Published on: September 25, 2011

14.0K
Predictive Immune Modeling of Solid Tumors
08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

7.0K

Area of Science:

  • Immunology
  • Bioinformatics
  • Genomics

Background:

  • Immune cell infiltration in colon tumors impacts cancer progression and therapeutic outcomes.
  • Understanding the specific immune cell composition within the tumor microenvironment is crucial for effective treatment strategies.

Purpose of the Study:

  • To identify and validate genetic markers for 25 distinct immune cell types in the normal colon.
  • To reveal quantitative differences between these immune cell types.
  • To establish machine learning-based classification rules for immune cell identification.

Main Methods:

  • Analysis of single-cell RNA sequencing data from 41,650 colon immune cells, encompassing 22,164 genes.
  • Application of five machine learning feature ranking algorithms (LASSO, LGBM, MCFS, mRMR, Random Forest) to identify important gene features.
  • Utilized incremental feature selection combined with decision tree and random forest algorithms to pinpoint critical genetic markers.

Main Results:

  • Identified specific marker genes for various immune cell subtypes, including KLRB1 (CD4 T cells), RPL30 (IgA plasma cells), and JCHAIN (IgG-producing B cells).
  • Confirmed differential gene expression and involvement in immune processes for the identified markers.
  • Developed quantitative classification rules using decision trees to distinguish between immune cell types.

Conclusions:

  • The identified genetic markers and classification rules provide a valuable resource for analyzing colon cancer immune microenvironments.
  • These findings can inform and enhance the development of targeted clinical immunotherapies for colon cancer.