Radiogenomic Bipartite Graph Representation Learning for Alzheimer's Disease Detection

TL;DR

This paper introduces a novel radiogenomic bipartite graph learning framework that effectively classifies Alzheimer's disease stages using MRI and gene expression data, revealing key genetic factors involved.

Contribution

It presents a new heterogeneous bipartite graph approach integrating imaging and genomic data for Alzheimer's detection, with effective classification and gene identification capabilities.

Findings

Accurate classification of AD, MCI, and CN stages.

Identification of significant genes for each class.

Effective use of small datasets for complex disease classification.

Abstract

Imaging and genomic data offer distinct and rich features, and their integration can unveil new insights into the complex landscape of diseases. In this study, we present a novel approach utilizing radiogenomic data including structural MRI images and gene expression data, for Alzheimer's disease detection. Our framework introduces a novel heterogeneous bipartite graph representation learning featuring two distinct node types: genes and images. The network can effectively classify Alzheimer's disease (AD) into three distinct stages:AD, Mild Cognitive Impairment (MCI), and Cognitive Normal (CN) classes, utilizing a small dataset. Additionally, it identified which genes play a significant role in each of these classification groups. We evaluate the performance of our approach using metrics including classification accuracy, recall, precision, and F1 score. The proposed technique holds potential for extending to radiogenomic-based classification to other diseases.