Autism spectrum disorder diagnosis using graph attention network based on spatial-constrained sparse functional brain networks
C Yang, P Wang, J Tan, Q Liu, X Li - Computers in biology and medicine, 2021 - Elsevier
The accurate diagnosis of autism spectrum disorder (ASD), a common mental disease in
children, has always been an important task in clinical practice. In recent years, the use of …
children, has always been an important task in clinical practice. In recent years, the use of …
Classifying ASD based on time-series fMRI using spatial–temporal transformer
As the prevalence of autism spectrum disorder (ASD) increases globally, more and more
patients need to receive timely diagnosis and treatment to alleviate their suffering. However …
patients need to receive timely diagnosis and treatment to alleviate their suffering. However …
[HTML][HTML] Technologies to support the diagnosis and/or treatment of neurodevelopmental disorders: A systematic review
In recent years, there has been a great interest in utilizing technology in mental health
research. The rapid technological development has encouraged researchers to apply …
research. The rapid technological development has encouraged researchers to apply …
MAMF-GCN: Multi-scale adaptive multi-channel fusion deep graph convolutional network for predicting mental disorder
J Pan, H Lin, Y Dong, Y Wang, Y Ji - Computers in biology and medicine, 2022 - Elsevier
Purpose Existing diagnoses of mental disorders rely on symptoms, patient descriptions, and
scales, which are not objective enough. We attempt to explore an objective diagnostic …
scales, which are not objective enough. We attempt to explore an objective diagnostic …
[HTML][HTML] Graph representation learning and its applications: a survey
Graphs are data structures that effectively represent relational data in the real world. Graph
representation learning is a significant task since it could facilitate various downstream …
representation learning is a significant task since it could facilitate various downstream …
Mapping multi-modal brain connectome for brain disorder diagnosis via cross-modal mutual learning
Recently, the study of multi-modal brain connectome has recorded a tremendous increase
and facilitated the diagnosis of brain disorders. In this paradigm, functional and structural …
and facilitated the diagnosis of brain disorders. In this paradigm, functional and structural …
Contrastive brain network learning via hierarchical signed graph pooling model
Recently, brain networks have been widely adopted to study brain dynamics, brain
development, and brain diseases. Graph representation learning techniques on brain …
development, and brain diseases. Graph representation learning techniques on brain …
Virtual adversarial training-based deep feature aggregation network from dynamic effective connectivity for MCI identification
Dynamic functional connectivity (dFC) network inferred from resting-state fMRI reveals
macroscopic dynamic neural activity patterns for brain disease identification. However, dFC …
macroscopic dynamic neural activity patterns for brain disease identification. However, dFC …
LncRNA-disease association identification using graph auto-encoder and learning to rank
Q Liang, W Zhang, H Wu, B Liu - Briefings in Bioinformatics, 2023 - academic.oup.com
Discovering the relationships between long non-coding RNAs (lncRNAs) and diseases is
significant in the treatment, diagnosis and prevention of diseases. However, current …
significant in the treatment, diagnosis and prevention of diseases. However, current …
Brain imaging-based machine learning in autism spectrum disorder: methods and applications
Autism spectrum disorder (ASD) is a neurodevelopmental condition with early childhood
onset and high heterogeneity. As the pathogenesis is still elusive, ASD diagnosis is …
onset and high heterogeneity. As the pathogenesis is still elusive, ASD diagnosis is …