Cargando…

VGAEDTI: drug-target interaction prediction based on variational inference and graph autoencoder

MOTIVATION: Accurate identification of Drug-Target Interactions (DTIs) plays a crucial role in many stages of drug development and drug repurposing. (i) Traditional methods do not consider the use of multi-source data and do not consider the complex relationship between data sources. (ii) How to bet...

Descripción completa

Detalles Bibliográficos
Autores principales:	Zhang, Yuanyuan, Feng, Yinfei, Wu, Mengjie, Deng, Zengqian, Wang, Shudong
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2023
Materias:	Research
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10324201/ https://www.ncbi.nlm.nih.gov/pubmed/37415176 http://dx.doi.org/10.1186/s12859-023-05387-w

Ejemplares similares

SGAEMDA: Predicting miRNA-Disease Associations Based on Stacked Graph Autoencoder
por: Wang, Shudong, et al.
Publicado: (2022)

Drug-target interaction prediction based on spatial consistency constraint and graph convolutional autoencoder
por: Chen, Peng, et al.
Publicado: (2023)

Drug repositioning based on heterogeneous networks and variational graph autoencoders
por: Lei, Song, et al.
Publicado: (2022)

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

Gene knockout inference with variational graph autoencoder learning single-cell gene regulatory networks
por: Yang, Yongjian, et al.
Publicado: (2023)

EFMSDTI: Drug-target interaction prediction based on an efficient fusion of multi-source data
por: Zhang, Yuanyuan, et al.
Publicado: (2022)

Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation
por: Kwon, Youngchun, et al.
Publicado: (2019)

Drug-protein interaction prediction via variational autoencoders and attention mechanisms
por: Zhang, Yue, et al.
Publicado: (2022)

Optimizing Variational Graph Autoencoder for Community Detection with Dual Optimization
por: Choong, Jun Jin, et al.
Publicado: (2020)

Synthetic electronic health records generated with variational graph autoencoders
por: Nikolentzos, Giannis, et al.
Publicado: (2023)

A Novel Approach for Drug-Target Interactions Prediction Based on Multimodal Deep Autoencoder
por: Wang, Huiqing, et al.
Publicado: (2020)

Inferring gene regulatory network from single-cell transcriptomes with graph autoencoder model
por: Wang, Jiacheng, et al.
Publicado: (2023)

Generating tertiary protein structures via interpretable graph variational autoencoders
por: Guo, Xiaojie, et al.
Publicado: (2021)

Malware detection framework based on graph variational autoencoder extracted embeddings from API-call graphs
por: Gunduz, Hakan
Publicado: (2022)

Graph Variational Autoencoder for Detector Reconstruction and Fast Simulation in High-Energy Physics
por: Hariri, Ali
Publicado: (2021)

MGAE-DC: Predicting the synergistic effects of drug combinations through multi-channel graph autoencoders
por: Zhang, Peng, et al.
Publicado: (2023)

Collective dynamics of repeated inference in variational autoencoder rapidly find cluster structure
por: Nagano, Yoshihiro, et al.
Publicado: (2020)

Predicting Drug Synergy and Discovering New Drug Combinations Based on a Graph Autoencoder and Convolutional Neural Network
por: Li, Huijun, et al.
Publicado: (2023)

Graph Autoencoder with Preserving Node Attribute Similarity
por: Lin, Mugang, et al.
Publicado: (2023)

GADTI: Graph Autoencoder Approach for DTI Prediction From Heterogeneous Network
por: Liu, Zhixian, et al.
Publicado: (2021)

MULGA, a unified multi-view graph autoencoder-based approach for identifying drug–protein interaction and drug repositioning
por: Ma, Jiani, et al.
Publicado: (2023)

Image-level trajectory inference of tau pathology using variational autoencoder for Flortaucipir PET
por: Hong, Jimin, et al.
Publicado: (2022)

Deep variational graph autoencoders for novel host-directed therapy options against COVID-19
por: Ray, Sumanta, et al.
Publicado: (2022)

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

GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder
por: Li, Lei, et al.
Publicado: (2021)

IRC-Safe Graph Autoencoder for Unsupervised Anomaly Detection
por: Atkinson, Oliver, et al.
Publicado: (2022)

MSGNN-DTA: Multi-Scale Topological Feature Fusion Based on Graph Neural Networks for Drug–Target Binding Affinity Prediction
por: Wang, Shudong, et al.
Publicado: (2023)

ScaffoldGVAE: scaffold generation and hopping of drug molecules via a variational autoencoder based on multi-view graph neural networks
por: Hu, Chao, et al.
Publicado: (2023)

Biologically informed variational autoencoders allow predictive modeling of genetic and drug-induced perturbations
por: Doncevic, Daria, et al.
Publicado: (2023)

Prediction of miRNA-disease associations in microbes based on graph convolutional networks and autoencoders
por: Liao, Qingquan, et al.
Publicado: (2023)

Quantum Variational AutoEncoder
por: Vinci, Walter
Publicado: (2018)

GSAMDA: a computational model for predicting potential microbe–drug associations based on graph attention network and sparse autoencoder
por: Tan, Yaqin, et al.
Publicado: (2022)

Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance
por: Tsan, Steven, et al.
Publicado: (2021)

Representation Learning: Recommendation With Knowledge Graph via Triple-Autoencoder
por: Geng, Yishuai, et al.
Publicado: (2022)

Inferring the basis of binaural detection with a modified autoencoder
por: Smith, Samuel S., et al.
Publicado: (2023)

A Novel Scalarized Scaffold Hopping Algorithm with Graph-Based Variational Autoencoder for Discovery of JAK1 Inhibitors
por: Yu, Yang, et al.
Publicado: (2021)

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

Prediction of Drug-Likeness Using Deep Autoencoder Neural Networks
por: Hu, Qiwan, et al.
Publicado: (2018)

Predicting Aging of Brain Metabolic Topography Using Variational Autoencoder
por: Choi, Hongyoon, et al.
Publicado: (2018)

Predicting chemotherapy response using a variational autoencoder approach
por: Wei, Qi, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_v16v2ogmt6qfpf8jcuba2v4bhq