Results 61 to 70 of about 5,438,478 (196)

ACP-GCN: The Identification of Anticancer Peptides Based on Graph Convolution Networks [PDF]

IEEE Access, 2020
Anticancer peptide (ACP) is a class of anti-cancer peptide which can inhibit and kill tumor cells. Identification of ACPs is of great significance for the development of new anti-cancer drugs. However, most of computational methods make predictions based on machine learning using hand-crafted features.
Bing Rao, Lichao Zhang, Guoying Zhang
openaire   +3 more sources

Convolution Based Graph Representation Learning from the Perspective of High Order Node Similarities

Mathematics, 2022
Nowadays, graph representation learning methods, in particular graph neural network methods, have attracted great attention and performed well in many downstream tasks. However, most graph neural network methods have a single perspective since they start
Xing Li, Qingsong Li, Wei Wei, Zhiming Zheng   +3 more
doaj   +1 more source

Efficient Screening of Organic Singlet Fission Molecules Using Graph Neural Networks

Advanced Science, EarlyView.
A high‐throughput screening framework based on graph neural networks (GNNs) and multi‐level validation facilitates the identification of singlet fission (SF) candidates. By efficiently predicting excitation energies across 20 million molecules, and integrating TDDFT calculations, synthetic accessibility assessments, and GW+BSE calculations, this ...
Li Fu, Longfei Lv, Fan Zhang, Si Zhou, Weiwei Gao, Jijun Zhao   +5 more
wiley   +1 more source

SemanticST: A Scalable Multi‐Contextual Graph Learning Framework for Uncovering Spatial Niches and Robust Multi‐Sample Integration in Spatial Transcriptomics

Advanced Science, EarlyView.
Technical limitations often let dominant signals overshadow rare cell types and fine‐grained heterogeneity in spatial transcriptomics. SemanticST, a graph neural network using multi‐semantic graph fusion and a novel min‐cut loss, recovers these subtle patterns.
Roxana Zahedi, Ahmadreza Argha, Nona Farbehi, Ivan Bakhshayeshi, Thantrira Porntaveetus, Youqiong Ye, Nigel H. Lovell, Hamid Alinejad‐Rokny   +7 more
wiley   +1 more source

Spatiotemporal Prediction of Landslide Displacement Using Graph Convolutional Network-Based Models: A Case Study of the Tangjiao 1# Landslide in Chongqing, China

Applied Sciences
Landslide displacement monitoring can directly reflect the deformation process of a landslide. Predicting landslide displacements using monitored time series data through deep learning is a useful method for landslide early warning.
Yingjie Sun, Ting Liu, Chao Zhang, Ning Xi, Honglei Wang   +4 more
doaj   +1 more source

Structural reinforcement-based graph convolutional networks

Connection Science, 2022
Graph Convolutional Network (GCN) is a tool for feature extraction, learning, and inference on graph data, widely applied in numerous scenarios. Despite the great success of GCN, it performs weakly under some application conditions, such as a multiple ...
Jisheng Qin, Qianqian Wang, Tao Tao
doaj   +1 more source

STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems [PDF]

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019
We propose a new STAcked and Reconstructed Graph Convolutional Networks (STAR-GCN) architecture to learn node representations for boosting the performance in recommender systems, especially in the cold start scenario. STAR-GCN employs a stack of GCN encoder-decoders combined with intermediate supervision to improve the final prediction performance ...
Jiani Zhang 0001, Xingjian Shi, Shenglin Zhao, Irwin King   +3 more
openaire   +3 more sources

A Unified Hierarchical Multiscale Fusion Framework for Drug–Target Affinity Prediction: From Benchmark Performance to Nanomolar Inhibitor Discovery

Advanced Science, EarlyView.
A multimodal fusion framework integrating sequence, atomic, and fragment representations captures drug–target interactions across multiple scales. The model delivers strong predictive performance and enables efficient virtual screening. Applied to hematopoietic progenitor kinase 1 (HPK1), it identifies structurally diverse inhibitors with nanomolar ...
Shuo Liu, Xiang Zhang, Haixia Feng, Yuquan Li, Xiaoqing Gong, Yong Liang, Xiaojun Yao, Huanxiang Liu   +7 more
wiley   +1 more source

A Dual-Branch Fusion of a Graph Convolutional Network and a Convolutional Neural Network for Hyperspectral Image Classification

Sensors
Semi-supervised graph convolutional networks (SSGCNs) have been proven to be effective in hyperspectral image classification (HSIC). However, limited training data and spectral uncertainty restrict the classification performance, and the computational ...
Pan Yang, Xinxin Zhang
doaj   +1 more source

LGD-GCN: Local and Global Disentangled Graph Convolutional Networks

, 2023
Disentangled Graph Convolutional Network (DisenGCN) is an encouraging framework to disentangle the latent factors arising in a real-world graph. However, it relies on disentangling information heavily from a local range (i.e., a node and its 1-hop ...
Yi, Xinping, Zhang, Rui, Guo, Jingwei, Huang, Kaizhu   +3 more
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