Construction of a diagnostic model for temporal lobe epilepsy using interpretable deep learning: disease-associated markers identification.

ABSTRACT:

Introduction

Temporal lobe epilepsy (TLE) represents a significant neurological disorder with complex genetic underpinnings. This study aimed to develop an interpretable deep learning diagnostic model for TLE and identify disease-associated markers.

Methods

Using RNA-seq and microarray data from 287 samples collected from eight GEO datasets, we constructed multiple machine learning algorithms including Deep Neural Networks (DNN), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Logistic Regression (LR), and K-Nearest Neighbors (KNN) to distinguish TLE from normal. SHapley Additive exPlanations (SHAP) and Kolmogorov-Arnold Networks (KAN) were employed to interpret the model and identify key genes associated with TLE pathogenesis.

Results

After comparative analy

SUBMITTER: Wang T 

PROVIDER: S-EPMC12615413 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature