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Related Experiment Video

Updated: Dec 12, 2025

Flexible Colonoscopy in Mice to Evaluate the Severity of Colitis and Colorectal Tumors Using a Validated Endoscopic Scoring System

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Published on: October 16, 2013

Detecting ulcerative colitis from colon samples using efficient feature selection and machine learning.

Hanieh Marvi Khorasani¹, Hamid Usefi², Lourdes Peña-Castillo³

¹Department of Computer Science, Memorial University, St. John's, NL, A1B3X5, Canada.

Scientific Reports

|August 15, 2020

Summary

This summary is machine-generated.

This study developed a machine learning model using gene expression data to detect ulcerative colitis (UC). The model accurately identifies active UC cases and shows improved performance over existing methods.

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Systematic Scoring Analysis for Intestinal Inflammation in a Murine Dextran Sodium Sulfate-Induced Colitis Model

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Published on: February 14, 2021

Area of Science:

Gastroenterology
Computational Biology
Genomics

Background:

Ulcerative colitis (UC) is a common inflammatory bowel disease (IBD) affecting the colon's mucosal layer.
Diagnosis relies on clinical, endoscopic, histologic, and laboratory findings.
Machine learning aids in disease diagnosis model development.

Purpose of the Study:

To develop a machine learning model for discriminating between healthy individuals and UC patients.
To utilize a novel feature selection algorithm (DRPT) and support vector machine (SVM) classifier.
To evaluate the model's performance on an independent dataset.

Main Methods:

A feature selection algorithm (DRPT) was combined with a support vector machine (SVM) classifier.
The model was trained on gene expression values of 32 genes from colon samples.
Validation was performed using an independent gene expression dataset from UC patients (active and inactive).

Main Results:

The model achieved perfect detection for active ulcerative colitis cases.
An average precision of 0.62 was obtained for inactive UC cases.
The developed model demonstrated superior performance compared to previous studies and BioDiscML.

Conclusions:

The developed DRPT-SVM model effectively discriminates between healthy subjects and UC patients.
This approach shows promise for improving ulcerative colitis diagnosis.
The model offers enhanced precision, particularly for active disease detection.