Cargando…

Interpreting the Mechanism of Synergism for Drug Combinations Using Attention-Based Hierarchical Graph Pooling

SIMPLE SUMMARY: This paper introduces a novel graph neural network (a hierarchical graph pooling model), SANEpool, to effectively detect core sub-networks of significant genes for predicting the synergy score of drug/drug combinations in cancer. SANEpool successfully addresses the limitations of the...

Descripción completa

Detalles Bibliográficos
Autores principales:	Dong, Zehao, Zhang, Heming, Chen, Yixin, Payne, Philip R. O., Li, Fuhai
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2023
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10486573/ https://www.ncbi.nlm.nih.gov/pubmed/37686486 http://dx.doi.org/10.3390/cancers15174210

Ejemplares similares

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

Hierarchical Pooling in Graph Neural Networks to Enhance Classification Performance in Large Datasets
por: Pham, Hai Van, et al.
Publicado: (2021)

Dynamic Heterogeneous Graph Embedding Using Hierarchical Attentions
por: Yang, Luwei, et al.
Publicado: (2020)

Predicting Anticancer Drug Response With Deep Learning Constrained by Signaling Pathways
por: Zhang, Heming, et al.
Publicado: (2021)

Graph Multihead Attention Pooling with Self-Supervised Learning
por: Wang, Yu, et al.
Publicado: (2022)

Highly accurate disease diagnosis and highly reproducible biomarker identification with PathFormer
por: Li, Fuhai, et al.
Publicado: (2023)

Multi-order graph attention network for water solubility prediction and interpretation
por: Lee, Sangho, et al.
Publicado: (2023)

Graph convolutional networks with hierarchical multi-head attention for aspect-level sentiment classification
por: Li, Xiaowen, et al.
Publicado: (2022)

BHGAttN: A Feature-Enhanced Hierarchical Graph Attention Network for Sentiment Analysis
por: Zhang, Junjun, et al.
Publicado: (2022)

Integrative network and transcriptomics-based approach predicts genotype- specific drug combinations for melanoma
por: Regan, Kelly E., et al.
Publicado: (2017)

Human Learning of Hierarchical Graphs
por: Xia, Xiaohuan, et al.
Publicado: (2023)

Malicious traffic detection combined deep neural network with hierarchical attention mechanism
por: Liu, Xiaoyang, et al.
Publicado: (2021)

GCMM: graph convolution network based on multimodal attention mechanism for drug repurposing
por: Zhang, Fan, et al.
Publicado: (2022)

The interpretation of graphs in physics
por: MacPherson, I S, et al.
Publicado: (1974)

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

Stepwise-Hierarchical Pooled Analysis for Synergistic Interpretation of Meta-analyses Involving Randomized and Observational Studies: Methodology Development
por: Shin, In-Soo, et al.
Publicado: (2021)

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

Hierarchical Graph Representation of Pharmacophore Models
por: Arthur, Garon, et al.
Publicado: (2020)

Graph Algorithms for Mixture Interpretation
por: Crysup, Benjamin, et al.
Publicado: (2021)

AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification
por: Yazdani-Jahromi, Mehdi, et al.
Publicado: (2022)

Scalable and Transferable Reinforcement Learning for Multi-Agent Mixed Cooperative–Competitive Environments Based on Hierarchical Graph Attention
por: Chen, Yining, et al.
Publicado: (2022)

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

Graph–sequence attention and transformer for predicting drug–target affinity
por: Yan, Xiangfeng, et al.
Publicado: (2022)

Renyi entropy driven hierarchical graph clustering
por: Oggier, Frédérique, et al.
Publicado: (2021)

Hierarchical graph learning for protein–protein interaction
por: Gao, Ziqi, et al.
Publicado: (2023)

KHGCN: Knowledge-Enhanced Recommendation with Hierarchical Graph Capsule Network
por: Chen, Fukun, et al.
Publicado: (2023)

Addendum to the Acknowledgments: Stepwise-Hierarchical Pooled Analysis for Synergistic Interpretation of Meta-analyses Involving Randomized and Observational Studies: Methodology Development
por: Shin, In-Soo, et al.
Publicado: (2021)

Bottom-Up and Top-Down Graph Pooling
por: Yang, Jia-Qi, et al.
Publicado: (2020)

Attention-Based Graph Evolution
por: Fan, Shuangfei, et al.
Publicado: (2020)

Hierarchical graphs for rule-based modeling of biochemical systems
por: Lemons, Nathan W, et al.
Publicado: (2011)

Hierarchical Information Clustering by Means of Topologically Embedded Graphs
por: Song, Won-Min, et al.
Publicado: (2012)

Dynamic Networks from Hierarchical Bayesian Graph Clustering
por: Park, Yongjin, et al.
Publicado: (2010)

Hierarchical Microbial Functions Prediction by Graph Aggregated Embedding
por: Hou, Yujie, et al.
Publicado: (2021)

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

Extended Hierarchical Fuzzy Interpreted Petri Net
por: Markiewicz, Michał, et al.
Publicado: (2021)

Rumor detection based on propagation graph neural network with attention mechanism
por: Wu, Zhiyuan, et al.
Publicado: (2020)

Spatial-Temporal Attention Mechanism and Graph Convolutional Networks for Destination Prediction
por: Li, Cong, et al.
Publicado: (2022)

Visual routines are associated with specific graph interpretations
por: Michal, Audrey L., et al.
Publicado: (2017)

Interpretable Neuron Structuring with Graph Spectral Regularization
por: Tong, Alexander, et al.
Publicado: (2020)

Enhanced mechanisms of pooling and channel attention for deep learning feature maps
por: Li, Hengyi, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_un5h708h704v7c5sgdeuikpagn