Article

Explainable machine learning models for glioma subtype classification and survival prediction

2025-06-25

Abstract excerpt

Gliomas are complex and heterogeneous brain tumors characterized by an unfavorable clinical course and a fatal prognosis, which can be improved by an early determination of tumor kind. Here, we develop explainable machine learning (ML) models for classifying three major glioma subtypes (astrocytoma, oligodendroglioma, and glioblastoma) and predicting survival rates based on RNA-seq data. Thirteen key genes ( TERT...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 569621c1-4754-5592-8d7e-d39303280c45
DOI: 10.1101/2025.06.24.661085

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Explainable machine learning models for glioma subtype classification and survival predictionDOI 10.1101/2025.06.24.661085Read

PathX-CNN: An Enhanced Explainable Convolutional Neural Network for Survival Prediction and Pathway Analysis in GlioblastomaSemantic proximity Read

Explainable Machine Learning to Identify Patient-specific Biomarkers for Lung CancerSemantic proximity Read

Bioinformatic analysis constructs an optimal prognostic index for survival-related variables (OPISV) based on whole-genome expression data in GlioblastomaSemantic proximity Read

Interpretable machine learning-driven biomarker identification and validation for Alzheimer’s diseaseSemantic proximity Read

Discovering Novel Prognostic Biomarkers of Hepatocellular Carcinoma using eXplainable Artificial IntelligenceSemantic proximity Read

Next-Generation Grade and Survival Expression Biomarkers of Human Gliomas Based on Algorithmically Reconstructed Molecular Pathways.Semantic proximity Read

Explainable autoencoder-based representation learning for gene expression dataSemantic proximity Read

An Explainable machine learning model for predicting response to targeted therapy and immunotherapy in advanced hepatocellular carcinoma: a multicentric studySemantic proximity Read

Explainable artificial intelligence to predict and identify prostate cancer tissue by gene expressionSemantic proximity Read

Machine Learning based Survival Group Prediction in Glioblastoma<strong> </strong>Semantic proximity Read

Explainable AI for the Prediction of Anticancer Drug Resistance Mechanisms in Human CancersSemantic proximity Read

An Interpretable Machine Learning Model for Predicting Overall Survival After Curative Hepatectomy in Hepatocellular CarcinomaSemantic proximity Read