Feature Selection in Cancer Classification: Utilizing Explainable Artificial Intelligence to Uncover Influential Genes in Machine Learning Models

Feature Selection in Cancer Classification: Utilizing Explainable Artificial Intelligence to Uncover Influential Genes in Machine Learning Models

This study investigates the use of machine learning (ML) models combined with explainable artificial intelligence (XAI) techniques to identify the most influential genes in the classification of five recurrent cancer types in women: breast cancer (BRCA), lung adenocarcinoma (LUAD), thyroid cancer (T...

Full description

Saved in:
Bibliographic Details
Main Authors: Matheus Dalmolin, Karolayne S. Azevedo, Luísa C. de Souza, Caroline B. de Farias, Martina Lichtenfels, Marcelo A. C. Fernandes
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:AI
Subjects:
explainable AI
machine learning
feature selection
RNA-seq
cancer
SHAP
Online Access:https://www.mdpi.com/2673-2688/6/1/2
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.mdpi.com/2673-2688/6/1/2

Similar Items