Graph signal processing, graph neural network and graph learning on biological data: a systematic review
Graph networks can model data observed across different levels of biological systems that
span from population graphs (with patients as network nodes) to molecular graphs that …
span from population graphs (with patients as network nodes) to molecular graphs that …
Data-driven graph construction and graph learning: A review
A graph is one of important mathematical tools to describe ubiquitous relations. In the
classical graph theory and some applications, graphs are generally provided in advance, or …
classical graph theory and some applications, graphs are generally provided in advance, or …
[HTML][HTML] Hybrid high-order functional connectivity networks using resting-state functional MRI for mild cognitive impairment diagnosis
Conventional functional connectivity (FC), referred to as low-order FC, estimates temporal
correlation of the resting-state functional magnetic resonance imaging (rs-fMRI) time series …
correlation of the resting-state functional magnetic resonance imaging (rs-fMRI) time series …
Multicenter and multichannel pooling GCN for early AD diagnosis based on dual-modality fused brain network
For significant memory concern (SMC) and mild cognitive impairment (MCI), their
classification performance is limited by confounding features, diverse imaging protocols, and …
classification performance is limited by confounding features, diverse imaging protocols, and …
Graph convolution network with similarity awareness and adaptive calibration for disease-induced deterioration prediction
Graph convolution networks (GCN) have been successfully applied in disease prediction
tasks as they capture interactions (ie, edges and edge weights on the graph) between …
tasks as they capture interactions (ie, edges and edge weights on the graph) between …
Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification
Brain functional connectivity (FC) extracted from resting‐state fMRI (RS‐fMRI) has become a
popular approach for diagnosing various neurodegenerative diseases, including …
popular approach for diagnosing various neurodegenerative diseases, including …
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 …
[HTML][HTML] Multiple measurement analysis of resting-state fMRI for ADHD classification in adolescent brain from the ABCD study
Z Wang, X Zhou, Y Gui, M Liu, H Lu - Translational Psychiatry, 2023 - nature.com
Attention deficit hyperactivity disorder (ADHD) is one of the most common psychiatric
disorders in school-aged children. Its accurate diagnosis looks after patients' interests well …
disorders in school-aged children. Its accurate diagnosis looks after patients' interests well …
Principles and open questions in functional brain network reconstruction
O Korhonen, M Zanin, D Papo - Human Brain Mapping, 2021 - Wiley Online Library
Graph theory is now becoming a standard tool in system‐level neuroscience. However,
endowing observed brain anatomy and dynamics with a complex network representation …
endowing observed brain anatomy and dynamics with a complex network representation …
Self-calibrated brain network estimation and joint non-convex multi-task learning for identification of early Alzheimer's disease
Detection of early stages of Alzheimer's disease (AD)(ie, mild cognitive impairment (MCI)) is
important to maximize the chances to delay or prevent progression to AD. Brain connectivity …
important to maximize the chances to delay or prevent progression to AD. Brain connectivity …