Article

A Biologically Interpretable Graph Convolutional Network to Link Genetic Risk Pathways and Neuroimaging Markers of Disease

2021-05-30

Abstract excerpt

<h4> A bstract </h4> We propose a novel end-to-end framework for whole-brain and whole-genome imaging-genetics. Our genetics network uses hierarchical graph convolution and pooling operations to embed subject-level data onto a low-dimensional latent space. The hierarchical network implicitly tracks the convergence of genetic risk across well-established biological pathways, while an attention mechanism automati...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: e469a760-c63d-5290-8a70-295ba5c32465
DOI: 10.1101/2021.05.28.446066

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

A Biologically Interpretable Graph Convolutional Network to Link Genetic Risk Pathways and Neuroimaging Markers of DiseaseDOI 10.1101/2021.05.28.446066Read

Deep Mediation Analysis for Multimodal Genotype-Imaging Associations with Disease PhenotypesSemantic proximity Read

G2DBridge: A Multimodal Framework Linking Genetics to Disease through Imaging IntermediatesSemantic proximity Read

Joint modeling of imaging and genetics.Semantic proximity Read

EIGNN: An Explainable Imaging-Genetic Neural Network for Robust Alzheimer's Disease Risk Prediction.Semantic proximity Read

Discovering disease genetic variation impacting gene expression in 103 brain tissues with the Brain Ontology Expression (BRONTE) graph neural network modelSemantic proximity Read

Combining Neuroimaging and Omics Datasets for Disease Classification Using Graph Neural NetworksSemantic proximity Read

Multi-Stage Graph Attention Networks for Interpretable Alzheimer’s Disease Classification from Genome-Wide Association DataSemantic proximity Read

Derivation and utility of schizophrenia polygenic risk associated multimodal MRI frontotemporal network.Semantic proximity Read

Functional neuroimaging and schizophrenia: a view towards effective connectivity modeling and polygenic risk.Semantic proximity Read

PRS-Net: Interpretable polygenic risk scores via geometric learningSemantic proximity Read

Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging.Semantic proximity Read

Enhancing schizophrenia phenotype prediction from genotype data through knowledge-driven deep neural network models.Semantic proximity Read