Cargando…

omicsGAT: Graph Attention Network for Cancer Subtype Analyses

The use of high-throughput omics technologies is becoming increasingly popular in all facets of biomedical science. The mRNA sequencing (RNA-seq) method reports quantitative measures of more than tens of thousands of biological features. It provides a more comprehensive molecular perspective of stud...

Descripción completa

Detalles Bibliográficos
Autores principales:	Baul, Sudipto, Ahmed, Khandakar Tanvir, Filipek, Joseph, Zhang, Wei
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2022
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9499656/ https://www.ncbi.nlm.nih.gov/pubmed/36142140 http://dx.doi.org/10.3390/ijms231810220

Ejemplares similares

TemporalGAT: Attention-Based Dynamic Graph Representation Learning
por: Fathy, Ahmed, et al.
Publicado: (2020)

LaGAT: link-aware graph attention network for drug–drug interaction prediction
por: Hong, Yue, et al.
Publicado: (2022)

sAMPpred-GAT: prediction of antimicrobial peptide by graph attention network and predicted peptide structure
por: Yan, Ke, et al.
Publicado: (2022)

GAT-LI: a graph attention network based learning and interpreting method for functional brain network classification
por: Hu, Jinlong, et al.
Publicado: (2021)

FraGAT: a fragment-oriented multi-scale graph attention model for molecular property prediction
por: Zhang, Ziqiao, et al.
Publicado: (2021)

Attention-Based Graph Neural Network for Label Propagation in Single-Cell Omics
por: Bhadani, Rahul, et al.
Publicado: (2023)

GOAT: Gene-level biomarker discovery from multi-Omics data using graph ATtention neural network for eosinophilic asthma subtype
por: Jeong, Dabin, et al.
Publicado: (2023)

Molecular Subtyping of Cancer Based on Robust Graph Neural Network and Multi-Omics Data Integration
por: Yin, Chaoyi, et al.
Publicado: (2022)

MoGCN: A Multi-Omics Integration Method Based on Graph Convolutional Network for Cancer Subtype Analysis
por: Li, Xiao, et al.
Publicado: (2022)

MRGCN: cancer subtyping with multi-reconstruction graph convolutional network using full and partial multi-omics dataset
por: Yang, Bo, et al.
Publicado: (2023)

GAT-FD: An integrated MATLAB toolbox for graph theoretical analysis of task-related functional dynamics
por: Cao, Meng, et al.
Publicado: (2022)

GAT: A Graph-Theoretical Analysis Toolbox for Analyzing Between-Group Differences in Large-Scale Structural and Functional Brain Networks
por: Hosseini, S. M. Hadi, et al.
Publicado: (2012)

Localization and Function of GABA Transporters GAT-1 and GAT-3 in the Basal Ganglia
por: Jin, Xiao-Tao, et al.
Publicado: (2011)

Recursive integration of synergised graph representations of multi-omics data for cancer subtypes identification
por: Madhumita, et al.
Publicado: (2022)

Evolutionary History of the GABA Transporter (GAT) Group Revealed by Marine Invertebrate GAT-1
por: Kinjo, Azusa, et al.
Publicado: (2013)

Zwart Gat-wetenschappers bedreigd
Publicado: (2008)

Plasma membrane transporters GAT-1 and GAT-3 contribute to heterogeneity of GABAergic synapses in neocortex
por: Melone, Marcello, et al.
Publicado: (2014)

SADLN: Self-attention based deep learning network of integrating multi-omics data for cancer subtype recognition
por: Sun, Qiuwen, et al.
Publicado: (2023)

Gated Graph Attention Network for Cancer Prediction
por: Qiu, Linling, et al.
Publicado: (2021)

Multi-Modality Emotion Recognition Model with GAT-Based Multi-Head Inter-Modality Attention
por: Fu, Changzeng, et al.
Publicado: (2020)

moBRCA-net: a breast cancer subtype classification framework based on multi-omics attention neural networks
por: Choi, Joung Min, et al.
Publicado: (2023)

Attention-Based Aggregation Graph Networks for Knowledge Graph Information Transfer
por: Zhao, Ming, et al.
Publicado: (2020)

Forward Genetics Reveals a gatC-gatA Fusion Polypeptide Causes Mistranslation and Rifampicin Tolerance in Mycobacterium smegmatis
por: Cai, Rong-Jun, et al.
Publicado: (2020)

Pharmacological evaluation of enantiomerically separated positive allosteric modulators of cannabinoid 1 receptor, GAT591 and GAT593
por: Brandt, Asher L., et al.
Publicado: (2022)

Recipe Recommendation With Hierarchical Graph Attention Network
por: Tian, Yijun, et al.
Publicado: (2022)

Combining Neuroimaging and Omics Datasets for Disease Classification Using Graph Neural Networks
por: Chan, Yi Hao, et al.
Publicado: (2022)

Expression of functional inhibitory neurotransmitter transporters GlyT1, GAT-1, and GAT-3 by astrocytes of inferior colliculus and hippocampus
por: Ghirardini, Elsa, et al.
Publicado: (2018)

EGAT: Extended Graph Attention Network for Pedestrian Trajectory Prediction
por: Kong, Wei, et al.
Publicado: (2021)

A Reappraisal of GAT-1 Localization in Neocortex
por: Fattorini, Giorgia, et al.
Publicado: (2020)

Molecular Property Prediction by Combining LSTM and GAT
por: Xu, Lei, et al.
Publicado: (2023)

Graph Attention Site Prediction (GrASP): Identifying Druggable Binding Sites Using Graph Neural Networks with Attention
por: Smith, Zachary, et al.
Publicado: (2023)

Prediction of circRNA-Disease Associations Based on the Combination of Multi-Head Graph Attention Network and Graph Convolutional Network
por: Cao, Ruifen, et al.
Publicado: (2022)

SLGAT: Soft Labels Guided Graph Attention Networks
por: Wang, Yubin, et al.
Publicado: (2020)

Discovering latent node Information by graph attention network
por: Gu, Weiwei, et al.
Publicado: (2021)

Crystal graph attention networks for the prediction of stable materials
por: Schmidt, Jonathan, et al.
Publicado: (2021)

Ligand Binding Prediction Using Protein Structure Graphs and Residual Graph Attention Networks
por: Pandey, Mohit, et al.
Publicado: (2022)

MultiGATAE: A Novel Cancer Subtype Identification Method Based on Multi-Omics and Attention Mechanism
por: Zhang, Ge, et al.
Publicado: (2022)

GraphOmics: an interactive platform to explore and integrate multi-omics data
por: Wandy, Joe, et al.
Publicado: (2021)

Network-based drug sensitivity prediction
por: Ahmed, Khandakar Tanvir, et al.
Publicado: (2020)

Graph Attention Interaction Aggregation Network for Click-Through Rate Prediction
por: Zhang, Wei, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_3nfrun8i384682o3ccc2bvctcd