Combining neuroimaging and omics datasets for disease classification using graph neural networks
Both neuroimaging and genomics datasets are often gathered for the detection of
neurodegenerative diseases. Huge dimensionalities of neuroimaging data as well as omics …
neurodegenerative diseases. Huge dimensionalities of neuroimaging data as well as omics …
Braingnn: Interpretable brain graph neural network for fmri analysis
Understanding which brain regions are related to a specific neurological disorder or
cognitive stimuli has been an important area of neuroimaging research. We propose …
cognitive stimuli has been an important area of neuroimaging research. We propose …
Brain functional activity-based classification of autism spectrum disorder using an attention-based graph neural network combined with gene expression
Autism spectrum disorder (ASD) is a complex brain neurodevelopmental disorder related to
brain activity and genetics. Most of the ASD diagnostic models perform feature selection at …
brain activity and genetics. Most of the ASD diagnostic models perform feature selection at …
GATE: Graph CCA for temporal self-supervised learning for label-efficient fMRI analysis
In this work, we focus on the challenging task, neuro-disease classification, using functional
magnetic resonance imaging (fMRI). In population graph-based disease analysis, graph …
magnetic resonance imaging (fMRI). In population graph-based disease analysis, graph …
Estimating functional brain networks by incorporating a modularity prior
Functional brain network analysis has become one principled way of revealing informative
organization architectures in healthy brains, and providing sensitive biomarkers for …
organization architectures in healthy brains, and providing sensitive biomarkers for …
Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications
Graphs are a powerful tool for representing and analyzing unstructured, non-Euclidean data
ubiquitous in the healthcare domain. Two prominent examples are molecule property …
ubiquitous in the healthcare domain. Two prominent examples are molecule property …
Inferring brain causal and temporal-lag networks for recognizing abnormal patterns of dementia
Brain functional network analysis has become a popular method to explore the laws of brain
organization and identify biomarkers of neurological diseases. However, it is still a …
organization and identify biomarkers of neurological diseases. However, it is still a …
Sparse interpretation of graph convolutional networks for multi-modal diagnosis of alzheimer's disease
The interconnected quality of brain regions in neurological disease has immense
importance for the development of biomarkers and diagnostics. While Graph Convolutional …
importance for the development of biomarkers and diagnostics. While Graph Convolutional …
The importance of anti-correlations in graph theory based classification of autism spectrum disorder
A Kazeminejad, RC Sotero - Frontiers in neuroscience, 2020 - frontiersin.org
With the release of the multi-site Autism Brain Imaging Data Exchange, many researchers
have applied machine learning methods to distinguish between healthy subjects and autistic …
have applied machine learning methods to distinguish between healthy subjects and autistic …
Structural deep brain network mining
Mining from neuroimaging data is becoming increasingly popular in the field of healthcare
and bioinformatics, due to its potential to discover clinically meaningful structure patterns …
and bioinformatics, due to its potential to discover clinically meaningful structure patterns …