Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN by Ming Zeng, Min Wang, Fuqiang Xie, Zhiwei Ji

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Epilepsy EEG Seizure Prediction Based on the Combination of Graph Convolutional Neural Network Combined with Long- and Short-Term Memory Cell Network by Zhejun Kuang, Simin Liu, Jian Zhao, Liu Wang, Yunkai Li

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Lightweight pose estimation spatial-temporal enhanced graph convolutional model for miner behavior recognition by WANG Jianfang, DUAN Siyuan, PAN Hongguang, JING Ningbo

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On Traffic Prediction With Knowledge-Driven Spatial–Temporal Graph Convolutional Network Aided by Selected Attention Mechanism by Yuwen Qian, Tianyang Qiu, Chuan Ma, Yiyang Ni, Long Yuan, Xiangwei Zhou, Jun Li

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Enhanced Community Detection via Convolutional Neural Network: A Modified Approach Based on MRFasGCN Algorithm by Puneet Kumar, Dalwinder Singh, Mamoona Humayun, Ali Alqazzaz, Arun Malik, Ibrahim Alrashdi, Isha Batra, Ghadah Naif Alwakid

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Adaptive Graph Convolutional Network with Deep Sequence and Feature Correlation Learning for Porosity Prediction from Well-Logging Data by Long Hao, Xun Wang, Yunlong Dong, Peizhi Zhao, Peifu Han, Xue Li, Fengrui Jing, Chuchu Zhai

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Integrating Message Content and Propagation Path for Enhanced False Information Detection Using Bidirectional Graph Convolutional Neural Networks by Jie Hu, Mei Yang, Bingbing Tang, Jianjun Hu

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DeepMoIC: multi-omics data integration via deep graph convolutional networks for cancer subtype classification by Jiecheng Wu, Zhaoliang Chen, Shunxin Xiao, Genggeng Liu, Wenjie Wu, Shiping Wang

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Optimized Demand Forecasting for Bike-Sharing Stations Through Multi-Method Fusion and Gated Graph Convolutional Neural Networks by Hebin Guo, Kexin Li, Yutong Rou

Subjects: “…Gated graph convolutional neural network…”
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Discovery of novel TACE inhibitors using graph convolutional network, molecular docking, molecular dynamics simulation, and Biological evaluation. by Muhammad Yasir, Jinyoung Park, Eun-Taek Han, Jin-Hee Han, Won Sun Park, Mubashir Hassan, Andrzej Kloczkowski, Wanjoo Chun

“…Using RDKit, a cheminformatics toolkit, we extracted molecular features from these compounds. We applied the GraphConvMol model within the DeepChem framework, which utilizes graph convolutional networks, to build a predictive model based on the DUD-E datasets. …”
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EDG-Net: Edge-Enhanced Dynamic Graph Convolutional Network for Remote Sensing Scene Classification of Mining-Disturbed Land by Xianju Li, Pan Kong, Weitao Chen, Wenxi He, Jian Feng, Jiangyuan Wang

Subjects: “…Graph convolution network (GCN)…”
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Multi-Source Data-Driven Local-Global Dynamic Multi-Graph Convolutional Network for Bike-Sharing Demands Prediction by Juan Chen, Rui Huang

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AD-GCN: A novel graph convolutional network integrating multi-omics data for enhanced Alzheimer’s disease diagnosis by Zilu Li, Qingyun Li, Xiaoqing Li, Wei Luo, Haiyan Guo, Chunyan Zhao, Canzhen Yang, Anke Xie, Kai Hu, Yangfan Guo

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Leveraging commonality across multiple tissue slices for enhanced whole slide image classification using graph convolutional networks by Sakonporn Noree, Willmer Rafell Quinones Robles, Young Sin Ko, Mun Yong Yi

“…Our method constructs graphs for each tissue slice, extracts relevant features, and connects these graphs based on spatial relationships and feature similarities, creating a comprehensive representation of the entire tissue sample, which is then used for WSI classification using graph convolutional networks. …”
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Examining the complex and cumulative effects of environmental exposures on noise perception through interpretable spatio-temporal graph convolutional networks by Liuyi Song, Mei-Po Kwan, Yang Liu

“…To address this gap, this study employs noise exposure as a case study and utilizes an interpretable spatio-temporal graph convolutional network (ST-GCN) framework to model the perception process in urban environments. …”
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MolAttnNet: A Predictive Model for Organic Drug Solubility Based on Graph Convolutional Networks and Transformer-Attention by Chenxu Wang, Yijun Feng, Zhejie Xu, Xiaohui Xu, Bangguo Peng

“…The framework comprises three specialized modules: a Graph Convolutional Network for extracting local molecular structural features, a multi-granularity attention mechanism for capturing both local and global molecular dependencies, and an adaptive LSTM with chemically-informed forget gates for selective feature retention and noise attenuation. …”
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A framework for continual learning in real-time traffic forecasting utilizing spatial–temporal graph convolutional recurrent networks by Mariam Labib Francies, Abeer Twakol Khalil, Hanan M. Amer, Mohamed Maher Ata

“…Although Deep Learning (DL) models demonstrate potential, their significant computational requirements and susceptibility to catastrophic forgetting limit their effectiveness in dynamic and real-time contexts, including traffic emergencies or evolving road networks. To address these challenges, this research presents an innovative framework known as the Continual Learning-based Spatial–Temporal Graph Convolutional Recurrent Neural Network (STGNN-CL) for persistent and accurate long-term traffic flow prediction. …”
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Identifying key genetic variants in Alzheimer’s disease progression using Graph Convolutional Networks (GCN) and biological impact analysis by Belal A. Hamed, Heba Mamdouh Farghaly, Ahmed Omar, Tarek Abd El-Hafeez

“…We present a novel deep learning framework integrating Single Nucleotide Polymorphism (SNP) data with Graph Convolutional Networks (GCNs) to predict gene-disease relationships in AD. …”
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Multi-Sensor Information Fusion with Multi-Scale Adaptive Graph Convolutional Networks for Abnormal Vibration Diagnosis of Rolling Mill by Rongrong Peng, Changfen Gong, Shuai Zhao

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Siamese Graph Convolutional Split-Attention Network with NLP based Social Sentimental Data for enhanced stock price predictions by Jayaraman Kumarappan, Elakkiya Rajasekar, Subramaniyaswamy Vairavasundaram, Ketan Kotecha, Ambarish Kulkarni

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