A comparative analysis of gene expression profiling by statistical and machine learning approaches.

ABSTRACT:

Motivation

Many machine learning (ML) models developed to classify phenotype from gene expression data provide interpretations for their decisions, with the aim of understanding biological processes. For many models, including neural networks, interpretations are lists of genes ranked by their importance for the predictions, with top-ranked genes likely linked to the phenotype. In this article, we discuss the limitations of such approaches using integrated gradient, an explainability method developed for neural networks, as an example.

Results

Experiments are performed on RNA sequencing data from public cancer databases. A collection of ML models, including multilayer perceptrons and graph neural networks, are trained to classify samples by cancer type. Gene rankings from in

SUBMITTER: Bontonou M 

PROVIDER: S-EPMC11783302 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature