Results 151 to 160 of about 5,438,478 (196)

Interpretable Drug–Target Prediction via Geometric Alignment: A Linear Alternative to Deep Architectural Complexity

Molecular Informatics, Volume 45, Issue 11, November 2026.
The Multi‐View Linear Manifold (MVLM) aligns frozen chemical and protein language model embeddings in a shared geometric space using two linear projections. Binding is scored by a single dot product. MVLM achieves state‐of‐the‐art drug–target interaction prediction on KIBA, performs competitively on Davis, and recovers kinase phylogenetic structure ...
Aashutosh Ghimire, Chris Alvin, Adam Bess, Supratik Mukhopadhyay   +3 more
wiley   +1 more source

GCN-RP: Graph Convolutional Network-Based Predictor for Network Connectivity Resilience

IEEE Access
Evaluating the connectivity resilience of real-world networks through attack simulations is a time-consuming process. This study proposes a method that trains Graph Convolutional Networks (GCNs) on synthetic graphs-generated from real graphs-to predict the resilience curves (Normalized Largest Connected Component, NLCC) of real networks. The method was
openaire   +2 more sources

Artificial Intelligence–Driven Graph Methods for Microbiome Research: A Systematic Review

WIREs Mechanisms of Disease, Volume 18, Issue 6, November/December 2026.
Multi‐omics and microbiome‐derived datasets were converted into deterministic and probabilistic network representations for downstream classical graph analysis and graph representation learning. This framework enables link prediction, relationship discovery, and node clustering to improve interpretation of complex biological interactions.
Cedrique Shum‐Tim, Paromita Islam, Jacqueline L. Boyajian, Ahmed Abosalha, Amal Kassab, Madison Santos, Kinaan Aamir Khan, Kiri Stern, Amin Emad, Satya Prakash   +9 more
wiley   +1 more source

TEA-GCN: Transformer-Enhanced Adaptive Graph Convolutional Network for Traffic Flow Forecasting

Traffic flow forecasting is crucial for improving urban traffic management and reducing resource consumption. Accurate traffic conditions prediction requires capturing the complex spatial-temporal dependencies inherent in traffic data.
Xiaoyu Li, Wenhui Zhang, Xiaxia He, Xiaodan Zhang   +3 more
core   +1 more source

Estimating Regional Snow Water Equivalent Using a Physics‐Informed Graph Convolution Network Model

Earth and Space Science, Volume 13, Issue 11, November 2026.
Abstract Snow water equivalent (SWE), which quantifies the amount of water in a snowpack, is critical for water supply forecasting. This study explores machine learning (ML) models' capability of representing the spatial distribution of SWE across the western United States.
Wei‐Ting Hung, Ziheng Sun, Bowen Cui, Keren Zhou, Nicoleta Cristea, Justin Pflug, Annie Burgess, Daniel Tong   +7 more
wiley   +1 more source

A Spatiotemporal Causal Machine Learning Framework for Quantifying the Impact of PM2.5 on Asthma at the Community Level

GeoHealth, Volume 10, Issue 11, November 2026.
Abstract Air pollution, particularly fine particulate matter with an aerodynamic diameter of 2.5 μm or smaller (PM2.5), is a leading environmental risk factor for respiratory diseases. While previous studies have investigated the biological mechanisms linking PM2.5 to asthma exacerbation, these studies are unable to scale for population‐level ...
Jing Li, Xuantong Wang, Hong Wei Chu
wiley   +1 more source

GCN-Code&StreetGraphFor298Cities

The package includes (1) Python scripts for the proposed GCN framework on street network analysis with a case study of urban polycentricity measurement, (2) Shapefile of top 128 streets for each city and txt file of corresponding dual graph as inputs of ...
Ding Ma (4667212)
core   +1 more source

TAA-GCN: A Temporally Aware Adaptive Graph Convolutional Network for Age Estimation

Pattern Recognition, 2023
This paper proposes a novel age estimation algorithm, the Temporally-Aware Adaptive Graph Convolutional Network (TAA-GCN). Using a new representation based on graphs, the TAA-GCN utilizes skeletal, posture, clothing, and facial information to enrich the ...
Peter Youngs, Scott T. Acton, Matthew Korban   +2 more
exaly   +1 more source

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On the Explainability of Graph Convolutional Network With GCN Tangent Kernel

Neural Computation, 2023
AbstractGraph convolutional network (GCN) is a powerful deep model in dealing with graph data. However, the explainability of GCN remains a difficult problem since the training behaviors for graph neural networks are hard to describe. In this work, we show that for GCN with wide hidden feature dimension, the output for semisupervised problem can be ...
Xianchen Zhou, Hongxia Wang
openaire   +3 more sources

GCN-ICF: Graph Convolution Networks for Item-based Recommendation

2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 2020
Item-based Collaborative Filtering (ICF) is wildly used recommendation method. Under the assumption that a user tends to choose an item similar to what she has interacted before, ICF uses items that a user has interacted to learn a user’s profile, and then makes recommendations by calculating the similarity between a target item and the user’s profile.
Kai Deng, Jiajin Huang, Jin Qin
openaire   +2 more sources