Graph-Based Fusion of Imaging, Genetic and Clinical Data for Degenerative Disease Diagnosis

Graph learning methods have achieved noteworthy performance in disease diagnosis due to their ability to represent unstructured information such as inter-subject relationships. While it has been shown that imaging, genetic and clinical data are crucial for degenerative disease diagnosis, existing me...

Full description

Saved in:
Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics 2024-01, Vol.21 (1), p.57-68
Main Authors: Guo, Rui, Tian, Xu, Lin, Hanhe, McKenna, Stephen, Li, Hong-Dong, Guo, Fei, Liu, Jin
Format: Article
Language:English
Subjects:
Alzheimer's disease
Benchmarks
Biomedical imaging
Clinical data
Data integration
Data mining
degenerative disease
Degenerative diseases
Diagnosis
Disease
Effectiveness
Feature extraction
Genetics
Graph convolutional networks
graph learning
imaging and genetic data
Information processing
Medical diagnosis
Medical imaging
Modules
Movement disorders
Neurodegenerative diseases
Neuroimaging
Parkinson's disease
Unstructured data
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Graph learning methods have achieved noteworthy performance in disease diagnosis due to their ability to represent unstructured information such as inter-subject relationships. While it has been shown that imaging, genetic and clinical data are crucial for degenerative disease diagnosis, existing methods rarely consider how best to use their relationships. How best to utilize information from imaging, genetic and clinical data remains a challenging problem. This study proposes a novel graph-based fusion (GBF) approach to meet this challenge. To extract effective imaging-genetic features, we propose an imaging-genetic fusion module which uses an attention mechanism to obtain modality-specific and joint representations within and between imaging and genetic data. Then, considering the effectiveness of clinical information for diagnosing degenerative diseases, we propose a multi-graph fusion module to further fuse imaging-genetic and clinical features, which adopts a learnable graph construction strategy and a graph ensemble method. Experimental results on two benchmarks for degenerative disease diagnosis (Alzheimers Disease Neuroimaging Initiative and Parkinson's Progression Markers Initiative) demonstrate its effectiveness compared to state-of-the-art graph-based methods. Our findings should help guide further development of graph-based models for dealing with imaging, genetic and clinical data.
ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2023.3335369