GAT-LI: a graph attention network based learning and interpreting method for functional brain network classification

Background Autism spectrum disorders (ASD) imply a spectrum of symptoms rather than a single phenotype. ASD could affect brain connectivity at different degree based on the severity of the symptom. Given their excellent learning capability, graph neural networks (GNN) methods have recently been used...

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Veröffentlicht in:	BMC bioinformatics Jg. 22; H. 1; S. 1 - 20
Hauptverfasser:	Hu, Jinlong, Cao, Lijie, Li, Tenghui, Dong, Shoubin, Li, Ping
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 22.07.2021 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Algorithms Analysis Autism Bioinformatics Biomedical and Life Sciences Biomedical data Brain Classification Computational Biology/Bioinformatics Computer Appl. in Life Sciences Construction methods Decision analysis Deep learning Disorders Functional brain networks Graph attention networks Graph neural networks Graph representations Graph theory Graphical representations Health aspects Identification methods Learning Life Sciences Machine learning Machine Learning and Artificial Intelligence in Bioinformatics Mental disorders Methodology Methodology Article Methods Microarrays Model interpretation Network analysis Neural networks Phenotypes Resting-state functional connectivity data Signs and symptoms Time series China Graph attention networks Model interpretation Functional brain networks Resting-state functional connectivity data Classification
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
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