Revealing Fibrosis Genes as Biomarkers of Ulcerative Colitis: A Bioinformatics Study Based on ScRNA and Bulk RNA Datasets.

ABSTRACT:

Objective

This study aimed to uncover biomarkers associated with fibroblasts to diagnose ulcerative colitis (UC) and predict sensitivity to TNFα inhibitors.

Methods

We identified fibrosis-related genes by analyzing eight bulk RNA and one single-cell RNA sequencing dataset from UC patients. Three machine learning algorithms were employed to identify common significant genes. We utilized five machine learning models, namely Random Forest (RF), Support Vector Machine (SVM), Xgboost, Multilayer Perceptron (MLP), and Logistic Regression, to develop diagnostic models for UC. Following hyperparameter tweaking using grid search, we evaluated Matthew's Correlation Coefficient (MCC) of each model on the validation set. Finally, we identified five hub genes in UC patients and evaluated

SUBMITTER: Wang Y 

PROVIDER: S-EPMC12376109 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature