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14 papers · ranked by Valyu relevance

01
PPubMed21 Apr 2023
Graph Representation Learning and Its Applications: A Survey
Van Thuy Hoang, Hyeon-Ju Jeon, Eun-Soon You, Yoewon Yoon + 3 more
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 tasks, such as node classification, link prediction, etc. Graph representation learning aims to map graph entities to low-dimensional…
95relevance
02
PPubMed26 Mar 2023
Graph Autoencoder with Preserving Node Attribute Similarity
Mugang Lin, Kunhui Wen, Xuanying Zhu, Huihuang Zhao + 4 more
'Yongpan Sheng' 'Hao Wang' 'Yixiang Fang'] The graph autoencoder (GAE) is a powerful graph representation learning tool in an unsupervised learning manner for graph data. However, most existing GAE-based methods typically focus on preserving the graph topological structure by reconstructing the adjacency matrix while…
94relevance
03
PPubMed2 Dec 2021
CellVGAE: an unsupervised scRNA-seq analysis workflow with graph attention networks
David Buterez, Ioana Bica, Ifrah Tariq, Helena Andrés-Terré + 2 more
In this work, we assume simple graphs (undirected, unweighted, without loops or multiple edges), defined as a tuple $G=(V,E)$, where $V$ is the set of vertices or nodes ${v_{0},v_{1},…}$ and $E$ is the set of edges between nodes, $E\subseteqV\timesV$. A common representation is given by a graph’s adjacency matrix A…
89relevance
04
PPubMed9 Dec 2022
Single-cell RNA-seq data analysis using graph autoencoders and graph attention networks Feng et al. 10.3389/fgene.2022.1003711
Xiang Feng, Fang Fang, Haixia Long, Rao Zeng + 1 more
With the development of high-throughput sequencing technology, the scale of single-cell RNA sequencing (scRNA-seq) data has surged. Its data are typically high-dimensional, with high dropout noise and high sparsity. Therefore, gene imputation and cell clustering analysis of scRNA-seq data is increasingly important.…
88relevance
05
PPubMed5 Oct 2024
scDFN: enhancing single-cell RNA-seq clustering with deep fusion networks
Tianxiang Liu, Cangzhi Jia, Yue Bi, Xudong Guo + 2 more
Single-cell ribonucleic acid sequencing (scRNA-seq) technology can be used to perform high-resolution analysis of the transcriptomes of individual cells. Therefore, its application has gained popularity for accurately analyzing the ever-increasing content of heterogeneous single-cell datasets. Central to interpreting…
87relevance
06
PPubMed27 Jun 2025
Identification of models describing gene expression data leveraging machine learning methods Identification of models describing gene expression data leveraging machine learning methods
Lucas F. Jansen Klomp, Elena Queirolo, Janine N. Post, Hil G. E. Meijer + 1 more
Identification of models describing gene expression data leveraging machine learning methods Identification of models describing gene expression data leveraging machine learning methods Authors: ['Lucas F. Jansen Klomp' 'Elena Queirolo' 'Janine N. Post' 'Hil G. E. Meijer' 'Christoph Brune'] Mechanistic ordinary…
86relevance
07
PPubMed22 Jan 2025
Ensemble graph auto-encoders for clustering and link prediction
Chengxin Xie, Jingui Huang, Yongjiang Shi, Hui Pang + 3 more
'Xiumei Wen' 'Marco Piangerelli'] Graph auto-encoders are a crucial research area within graph neural networks, commonly employed for generating graph embeddings while minimizing errors in unsupervised learning. Traditional graph auto-encoders focus on reconstructing minimal graph data loss to encode neighborhood…
85relevance
08
PPubMed17 May 2026
ANIMATE: Unsupervised Attributed Graph Anomaly Detection with Masked Graph Transformers
Jingtao Hu, Yi Zhang, Chengzhang Zhu, Changsheng Hou + 1 more
Attributed graphs have recently emerged as a powerful tool for representing diverse data in numerous real-world sensors. Among various applications, unsupervised graph anomaly detection (UGAD) aims to identify abnormal data that significantly deviate from the majority of normal nodes without label annotations. Hence…
82relevance
09
PPubMed18 May 2022
Malware detection framework based on graph variational autoencoder extracted embeddings from API-call graphs
Hakan Gunduz, Muhammad Aleem
Malware harms the confidentiality and integrity of the information that causes material and moral damages to institutions or individuals. This study proposed a malware detection model based on API-call graphs and used Graph Variational Autoencoder (GVAE) to reduce the size of graph node features extracted from Android…
82relevance
10
PPubMed9 Dec 2022
SGAEMDA: Predicting miRNA-Disease Associations Based on Stacked Graph Autoencoder
Shudong Wang, Boyang Lin, Yuanyuan Zhang, Sibo Qiao + 4 more
'Wenhao Wu' 'Chuanru Ren' 'Shixiong Zhang'] MicroRNA (miRNA)-disease association (MDA) prediction is critical for disease prevention, diagnosis, and treatment. Traditional MDA wet experiments, on the other hand, are inefficient and costly.Therefore, we proposed a multi-layer collaborative unsupervised training base…
81relevance
11
PPubMed11 Sep 2024
scVGATAE: A Variational Graph Attentional Autoencoder Model for Clustering Single-Cell RNA-seq Data
Lijun Liu, Xiaoyang Wu, Jun Yu, Yuduo Zhang + 3 more
'Edward L. Braun'] Simple Summary Due to the rapid development of single-cell RNA sequencing technology, the volume of single-cell RNA sequencing data has grown exponentially. Traditional clustering methods have proven increasingly difficult to cluster this large-scale and highly complex single-cell RNA sequencing…
80relevance
12
PPubMed14 Apr 2023
GRACE: Graph autoencoder based single-cell clustering through ensemble similarity learning GRACE: Graph autoencoder based single-cell clustering through ensemble similarity learning
Jun Seo Ha, Hyundoo Jeong, Achraf El Allali
Recent advances in single-cell sequencing techniques have enabled gene expression profiling of individual cells in tissue samples so that it can accelerate biomedical research to develop novel therapeutic methods and effective drugs for complex disease. The typical first step in the downstream analysis pipeline is…
78relevance
13
PPubMed3 Jun 2022
Representation Learning: Recommendation With Knowledge Graph <i> via </i> Triple-Autoencoder Geng et al. Recommendation With KG <i> via </i> Triple-Autoencoder
Yishuai Geng, Xiao Xiao, Xiaobing Sun, Yi Zhu
The last decades have witnessed a vast amount of interest and research in feature representation learning from multiple disciplines, such as biology and bioinformatics. Among all the real-world application scenarios, feature extraction from knowledge graph (KG) for personalized recommendation has achieved substantial…
76relevance
14
PPubMed26 Mar 2024
Graph-Based Audio Classification Using Pre-Trained Models and Graph Neural Networks
Andrés Eduardo Castro-Ospina, Miguel Angel Solarte-Sanchez, Laura Stella Vega-Escobar, Claudia Isaza + 2 more
'Laura Stella Vega-Escobar' 'Claudia Isaza' 'Juan David Martínez-Vargas' 'Hector Eduardo Roman'] Sound classification plays a crucial role in enhancing the interpretation, analysis, and use of acoustic data, leading to a wide range of practical applications, of which environmental sound analysis is one of the most…
60relevance

Results for graph autoencoder

Graph Representation Learning and Its Applications: A Survey

Graph Autoencoder with Preserving Node Attribute Similarity

CellVGAE: an unsupervised scRNA-seq analysis workflow with graph attention networks

Single-cell RNA-seq data analysis using graph autoencoders and graph attention networks Feng et al. 10.3389/fgene.2022.1003711

scDFN: enhancing single-cell RNA-seq clustering with deep fusion networks

Identification of models describing gene expression data leveraging machine learning methods Identification of models describing gene expression data leveraging machine learning methods

Ensemble graph auto-encoders for clustering and link prediction

ANIMATE: Unsupervised Attributed Graph Anomaly Detection with Masked Graph Transformers

Malware detection framework based on graph variational autoencoder extracted embeddings from API-call graphs

SGAEMDA: Predicting miRNA-Disease Associations Based on Stacked Graph Autoencoder

scVGATAE: A Variational Graph Attentional Autoencoder Model for Clustering Single-Cell RNA-seq Data

GRACE: Graph autoencoder based single-cell clustering through ensemble similarity learning GRACE: Graph autoencoder based single-cell clustering through ensemble similarity learning

Representation Learning: Recommendation With Knowledge Graph <i> via </i> Triple-Autoencoder Geng et al. Recommendation With KG <i> via </i> Triple-Autoencoder

Graph-Based Audio Classification Using Pre-Trained Models and Graph Neural Networks

Graph Representation Learning and Its Applications: A Survey

Graph Autoencoder with Preserving Node Attribute Similarity

CellVGAE: an unsupervised scRNA-seq analysis workflow with graph attention networks

Single-cell RNA-seq data analysis using graph autoencoders and graph attention networks Feng et al. 10.3389/fgene.2022.1003711

scDFN: enhancing single-cell RNA-seq clustering with deep fusion networks

Identification of models describing gene expression data leveraging machine learning methods Identification of models describing gene expression data leveraging machine learning methods

Ensemble graph auto-encoders for clustering and link prediction

ANIMATE: Unsupervised Attributed Graph Anomaly Detection with Masked Graph Transformers

Malware detection framework based on graph variational autoencoder extracted embeddings from API-call graphs

SGAEMDA: Predicting miRNA-Disease Associations Based on Stacked Graph Autoencoder

scVGATAE: A Variational Graph Attentional Autoencoder Model for Clustering Single-Cell RNA-seq Data

GRACE: Graph autoencoder based single-cell clustering through ensemble similarity learning GRACE: Graph autoencoder based single-cell clustering through ensemble similarity learning

Representation Learning: Recommendation With Knowledge Graph <i> via </i> Triple-Autoencoder Geng et al. Recommendation With KG <i> via </i> Triple-Autoencoder

Graph-Based Audio Classification Using Pre-Trained Models and Graph Neural Networks