The effect of node features on GCN-based brain network classification: an empirical study

Wang, Guangyu; Zhang, Limei; Qiao, Liyan

doi:10.7717/peerj.14835

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…GNNs can learn the features and relationships of cells and improve the performance of different tasks. Graph convolutional networks (GCNs) are GNNs applied to single cells and diseases [36][37][38][39][40]. GNNs have also been used to analyze scRNA-seq data, such as imputation and clustering [41][42][43].…”

Section: Introductionmentioning

confidence: 99%

scMGCN: A Multi-View Graph Convolutional Network for Cell Type Identification in scRNA-seq Data

Sun,

Qu,

Duan

et al. 2024

IJMS

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Single-cell RNA sequencing (scRNA-seq) data reveal the complexity and diversity of cellular ecosystems and molecular interactions in various biomedical research. Hence, identifying cell types from large-scale scRNA-seq data using existing annotations is challenging and requires stable and interpretable methods. However, the current cell type identification methods have limited performance, mainly due to the intrinsic heterogeneity among cell populations and extrinsic differences between datasets. Here, we present a robust graph artificial intelligence model, a multi-view graph convolutional network model (scMGCN) that integrates multiple graph structures from raw scRNA-seq data and applies graph convolutional networks with attention mechanisms to learn cell embeddings and predict cell labels. We evaluate our model on single-dataset, cross-species, and cross-platform experiments and compare it with other state-of-the-art methods. Our results show that scMGCN outperforms the other methods regarding stability, accuracy, and robustness to batch effects. Our main contributions are as follows: Firstly, we introduce multi-view learning and multiple graph construction methods to capture comprehensive cellular information from scRNA-seq data. Secondly, we construct a scMGCN that combines graph convolutional networks with attention mechanisms to extract shared, high-order information from cells. Finally, we demonstrate the effectiveness and superiority of the scMGCN on various datasets.

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Section: Introductionmentioning

confidence: 99%

scMGCN: A Multi-View Graph Convolutional Network for Cell Type Identification in scRNA-seq Data

Sun,

Qu,

Duan

et al. 2024

IJMS

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Integration of temporal & spatial properties of dynamic functional connectivity based on two-directional two-dimensional principal component analysis for disease analysis

Zhao,

Lv,

et al. 2024

PeerJ

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Dynamic functional connectivity, derived from resting-state functional magnetic resonance imaging (rs-fMRI), has emerged as a crucial instrument for investigating and supporting the diagnosis of neurological disorders. However, prevalent features of dynamic functional connectivity predominantly capture either temporal or spatial properties, such as mean and global efficiency, neglecting the significant information embedded in the fusion of spatial and temporal attributes. In addition, dynamic functional connectivity suffers from the problem of temporal mismatch, i.e., the functional connectivity of different subjects at the same time point cannot be matched. To address these problems, this article introduces a novel feature extraction framework grounded in two-directional two-dimensional principal component analysis. This framework is designed to extract features that integrate both spatial and temporal properties of dynamic functional connectivity. Additionally, we propose to use Fourier transform to extract temporal-invariance properties contained in dynamic functional connectivity. Experimental findings underscore the superior performance of features extracted by this framework in classification experiments compared to features capturing individual properties.

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The effect of node features on GCN-based brain network classification: an empirical study

Cited by 2 publications

References 24 publications

scMGCN: A Multi-View Graph Convolutional Network for Cell Type Identification in scRNA-seq Data

scMGCN: A Multi-View Graph Convolutional Network for Cell Type Identification in scRNA-seq Data

Integration of temporal & spatial properties of dynamic functional connectivity based on two-directional two-dimensional principal component analysis for disease analysis

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