Graph neural networks and their current applications in bioinformatics
XM Zhang, L Liang, L Liu, MJ Tang - Frontiers in genetics, 2021 - frontiersin.org
Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space,
perform particularly well in various tasks that process graph structure data. With the rapid …
perform particularly well in various tasks that process graph structure data. With the rapid …
A survey on graph neural networks and graph transformers in computer vision: A task-oriented perspective
Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …
Multi-modal graph learning for disease prediction
Benefiting from the powerful expressive capability of graphs, graph-based approaches have
been popularly applied to handle multi-modal medical data and achieved impressive …
been popularly applied to handle multi-modal medical data and achieved impressive …
Differentiable graph module (dgm) for graph convolutional networks
Graph deep learning has recently emerged as a powerful ML concept allowing to generalize
successful deep neural architectures to non-euclidean structured data. Such methods have …
successful deep neural architectures to non-euclidean structured data. Such methods have …
RA-GCN: Graph convolutional network for disease prediction problems with imbalanced data
Disease prediction is a well-known classification problem in medical applications. Graph
Convolutional Networks (GCNs) provide a powerful tool for analyzing the patients' features …
Convolutional Networks (GCNs) provide a powerful tool for analyzing the patients' features …
Auto-metric graph neural network based on a meta-learning strategy for the diagnosis of Alzheimer's disease
X Song, M Mao, X Qian - IEEE Journal of Biomedical and …, 2021 - ieeexplore.ieee.org
Alzheimer's disease (AD) is the most common cognitive disorder. In recent years, many
computer-aided diagnosis techniques have been proposed for AD diagnosis and …
computer-aided diagnosis techniques have been proposed for AD diagnosis and …
A survey of deep learning for alzheimer's disease
Alzheimer's and related diseases are significant health issues of this era. The
interdisciplinary use of deep learning in this field has shown great promise and gathered …
interdisciplinary use of deep learning in this field has shown great promise and gathered …
Latent-graph learning for disease prediction
Abstract Recently, Graph Convolutional Networks (GCNs) have proven to be a powerful
machine learning tool for Computer Aided Diagnosis (CADx) and disease prediction. A key …
machine learning tool for Computer Aided Diagnosis (CADx) and disease prediction. A key …
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 …
Diagnosis of glioblastoma multiforme progression via interpretable structure-constrained graph neural networks
Glioblastoma multiforme (GBM) is the most common type of brain tumors with high
recurrence and mortality rates. After chemotherapy treatment, GBM patients still show a high …
recurrence and mortality rates. After chemotherapy treatment, GBM patients still show a high …