Cargando…

gGATLDA: lncRNA-disease association prediction based on graph-level graph attention network

BACKGROUND: Long non-coding RNAs (lncRNAs) are related to human diseases by regulating gene expression. Identifying lncRNA-disease associations (LDAs) will contribute to diagnose, treatment, and prognosis of diseases. However, the identification of LDAs by the biological experiments is time-consumin...

Descripción completa

Detalles Bibliográficos
Autores principales:	Wang, Li, Zhong, Cheng
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2022
Materias:	Research
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8729153/ https://www.ncbi.nlm.nih.gov/pubmed/34983363 http://dx.doi.org/10.1186/s12859-021-04548-z

Ejemplares similares

Predicting lncRNA-protein interactions with bipartite graph embedding and deep graph neural networks
por: Ma, Yuzhou, et al.
Publicado: (2023)

Heterogeneous graph neural network for lncRNA-disease association prediction
por: Shi, Hong, et al.
Publicado: (2022)

Prediction of lncRNA–Disease Associations via Closest Node Weight Graphs of the Spatial Neighborhood Based on the Edge Attention Graph Convolutional Network
por: Li, Jianwei, et al.
Publicado: (2022)

Multiview Consensus Graph Learning for lncRNA–Disease Association Prediction
por: Tan, Haojiang, et al.
Publicado: (2020)

Predicting lncRNA-disease associations based on heterogeneous graph convolutional generative adversarial network
por: Lu, Zhonghao, et al.
Publicado: (2023)

MAGCNSE: predicting lncRNA-disease associations using multi-view attention graph convolutional network and stacking ensemble model
por: Liang, Ying, et al.
Publicado: (2022)

Predicting lncRNA–Protein Interaction With Weighted Graph-Regularized Matrix Factorization
por: Sun, Xibo, et al.
Publicado: (2021)

Graph Convolutional Network and Convolutional Neural Network Based Method for Predicting lncRNA-Disease Associations
por: Xuan, Ping, et al.
Publicado: (2019)

Graph embedding ensemble methods based on the heterogeneous network for lncRNA-miRNA interaction prediction
por: Zhao, Chengshuai, et al.
Publicado: (2020)

Heterogeneous graph inference based on similarity network fusion for predicting lncRNA–miRNA interaction
por: Fan, Yongxian, et al.
Publicado: (2020)

DeepWalk based method to predict lncRNA-miRNA associations via lncRNA-miRNA-disease-protein-drug graph
por: Yang, Long, et al.
Publicado: (2022)

Predicting lncRNA-miRNA Interaction via Graph Convolution Auto-Encoder
por: Huang, Yu-An, et al.
Publicado: (2019)

Novel link prediction for large-scale miRNA-lncRNA interaction network in a bipartite graph
por: Huang, Zhi-An, et al.
Publicado: (2018)

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)

TPGLDA: Novel prediction of associations between lncRNAs and diseases via lncRNA-disease-gene tripartite graph
por: Ding, Liang, et al.
Publicado: (2018)

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

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

Construction and Comprehensive Analysis of a Molecular Association Network via lncRNA–miRNA–Disease–Drug–Protein Graph
por: Guo, Zhen-Hao, et al.
Publicado: (2019)

Predicting miRNA-Disease Associations Based on Heterogeneous Graph Attention Networks
por: Ji, Cunmei, et al.
Publicado: (2021)

GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention Network
por: Bian, Chen, et al.
Publicado: (2021)

Single-cell RNA-seq data analysis using graph autoencoders and graph attention networks
por: Feng, Xiang, et al.
Publicado: (2022)

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

A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations
por: Shi, Zhuangwei, et al.
Publicado: (2021)

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

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

Using Graph Attention Network and Graph Convolutional Network to Explore Human CircRNA–Disease Associations Based on Multi-Source Data
por: Li, Guanghui, et al.
Publicado: (2022)

Predicting miRNA-disease associations based on graph attention network with multi-source information
por: Li, Guanghui, et al.
Publicado: (2022)

Prediction of Drug-Drug Interaction Using an Attention-Based Graph Neural Network on Drug Molecular Graphs
por: Feng, Yue-Hua, et al.
Publicado: (2022)

Struct2Graph: a graph attention network for structure based predictions of protein–protein interactions
por: Baranwal, Mayank, et al.
Publicado: (2022)

Predicting binary, discrete and continued lncRNA-disease associations via a unified framework based on graph regression
por: Shi, Jian-Yu, et al.
Publicado: (2017)

GBDTLRL2D Predicts LncRNA–Disease Associations Using MetaGraph2Vec and K-Means Based on Heterogeneous Network
por: Duan, Tao, et al.
Publicado: (2021)

Heterogeneous graph attention networks for drug virus association prediction
por: Long, Yahui, et al.
Publicado: (2022)

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

Attention-Based Graph Neural Network for Molecular Solubility Prediction
por: Ahmad, Waqar, et al.
Publicado: (2023)

Multimodal graph attention network for COVID-19 outcome prediction
por: Keicher, Matthias, et al.
Publicado: (2023)

AFTGAN: prediction of multi-type PPI based on attention free transformer and graph attention network
por: Kang, Yanlei, et al.
Publicado: (2023)

Spatiotemporal adaptive attention graph convolution network for city-level air quality prediction
por: Liu, Hexiang, et al.
Publicado: (2023)

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

Predicting miRNA-disease associations via layer attention graph convolutional network model
por: Han, Han, et al.
Publicado: (2022)

Fusion of KATZ measure and space projection to fast probe potential lncRNA-disease associations in bipartite graphs
por: Zhang, Yi, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_63kbsujsl7uhn1fcs7m45nelda