Cover Image

Inferred global dense residue transition graphs from primary structure sequences enable protein interaction prediction via directed graph convolutional neural networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ebeid, Islam Akef, Tang, Haoteng, Gu, Pengfei
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Enhancing protein‐ligand binding affinity prediction through sequential fusion of graph and convolutional neural networks
by: Yimin Yang, et al.
Published: (2024)

Integrating protein sequence embeddings with structure via graph-based deep learning for single-residue property prediction
by: Michalewicz, Kevin, et al.
Published: (2025)

PPISHES —an enhanced physicochemical approach for predicting protein interaction sites using graph neural networks
by: Darpan Khanna, et al.
Published: (2025)

Predicting fluid-structure interaction with graph neural networks
by: Gao, Rui, et al.
Published: (2022)

Metalearning traffic assignment for network disruptions with graph convolutional neural networks
by: Agriesti, Serio, et al.
Published: (2026)

Manifold limit for the training of shallow graph convolutional neural networks
by: Tengler, Johanna, et al.
Published: (2026)

sHGCN: Simplified hyperbolic graph convolutional neural networks
by: Arévalo, Pol, et al.
Published: (2025)

SaeGraphDTI: drug-target interaction prediction based on sequence attribute extraction and graph neural network.
by: Zhang, Qiaosheng, et al.
Published: (2025)

Automatic quality assessment of knee radiographs using knowledge graphs and convolutional neural networks
by: Qian Wang, et al.
Published: (2024)

Attention is all you need for boosting graph convolutional neural network
by: Wu, Yinwei
Published: (2024)

Representing Born effective charges with equivariant graph convolutional neural networks
by: Kutana, Alex, et al.
Published: (2024)

Advanced atom-level representations for protein flexibility prediction utilizing graph neural networks
by: Sarparast, Sina, et al.
Published: (2024)

Using graph neural networks to predict many-body interactions in amorphous materials
by: Ghomsheh, Mehryar Jannesari, et al.
Published: (2026)

Enhancing material property prediction with ensemble deep graph convolutional networks
by: Rahman, Chowdhury Mohammad Abid, et al.
Published: (2024)

Estimating the stability number of a random graph using convolutional neural networks
by: Davila, Randy
Published: (2024)

Upright adjustment with graph convolutional networks
by: Jung, Raehyuk, et al.
Published: (2024)

Brain tumor segmentation and survival time prediction using graph momentum fully convolutional network with modified Elman spike neural network
by: M. Ramkumar, et al.
Published: (2024)

A unifying primary framework for quantum graph neural networks from quantum graph states
by: Daskin, Ammar
Published: (2024)

Gene regulatory network inference algorithm based on spectral signed directed graph convolution
by: Xi, Rijie, et al.
Published: (2025)

Ranking protein-protein models with large language models and graph neural networks
by: Xu, Xiaotong, et al.
Published: (2024)

An innovative peptide toxicity prediction model based on multi-scale convolutional neural network and residual connection
by: Ren, Jingyi
Published: (2025)

Equivariant graph convolutional neural networks for the representation of homogenized anisotropic microstructural mechanical response
by: Patel, Ravi, et al.
Published: (2024)

Planar Novikov-Shubin invariant for adjacency matrices of structured directed dense random graphs
by: Krüger, Torben, et al.
Published: (2024)

Adapting a Segmentation Foundation Model for Medical Image Classification
by: Gu, Pengfei, et al.
Published: (2025)

Phonon predictions with E(3)-equivariant graph neural networks
by: Fang, Shiang, et al.
Published: (2024)

Prototype-enhanced prediction in graph neural networks for climate applications
by: Keshtmand, Nawid, et al.
Published: (2025)

ALNSynergy: a graph convolutional network with multi-representation alignment for drug synergy prediction
by: Yang, Xinxing, et al.
Published: (2023)

Nuclear mass predictions based on convolutional neural network
by: Lu, Yanhua, et al.
Published: (2024)

Predicting travel demand of a bike sharing system using graph convolutional neural networks
by: Behroozi, Ali, et al.
Published: (2024)

Informed along the road: roadway capacity driven graph convolution network for network-wide traffic prediction
by: Bian, Zilin, et al.
Published: (2024)

A substructure‐aware graph neural network incorporating relation features for drug–drug interaction prediction
by: Liangcheng Dong, et al.
Published: (2024)

Crystal structure prediction using graph neural combinatorial optimization
by: Gerolymatos, Stavros, et al.
Published: (2026)

The typical structure of dense claw-free graphs
by: Perkins, Will, et al.
Published: (2025)

Crowdsourced bug report severity prediction based on text and image understanding via heterogeneous graph convolutional networks
by: Yifan Wu, et al.
Published: (2024)

The inherent convolution property of quantum neural networks
by: Qu, Guangkai, et al.
Published: (2025)

Soil moisture forecasting in wireless sensor networks via spatiotemporal graph convolutional networks
by: Weixuan Wang, et al.
Published: (2025)

SELECTOR: Heterogeneous graph network with convolutional masked autoencoder for multimodal robust prediction of cancer survival
by: Pan, Liangrui, et al.
Published: (2024)

Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction
by: Qu, Gang, et al.
Published: (2021)

Dynamic graph neural networks for enhanced volatility prediction in financial markets
by: Kumar, Pulikandala Nithish, et al.
Published: (2024)

Effective backdoor attack on graph neural networks in link prediction tasks
by: Dai, Jiazhu, et al.
Published: (2024)