Results 171 to 180 of about 36,148 (258)

Attention aware edge-node exchange graph neural network

Dianxin kexue
An attention aware edge-node exchange graph neural network (AENN) model was proposed, which used edge-node switched convolutional graph neural network method for graph encoding in a graph structured data representation framework for semi supervised ...
Ruiqin WANG, Yimin HUANG, Qishun JI, Chaoyi WAN, Zhifeng ZHOU   +4 more
doaj   +2 more sources

Graph Convolutional Neural Network-Enabled Frontier Molecular Orbital Prediction: A Case Study with Neurotransmitters and Antidepressants. [PDF]

J Chem Inf Model
Monsia R, Gundry SC, Mohr ML, Smith MA, Bhattacharyya S, Hati S.   +5 more
europepmc   +1 more source

A New Regression Model for Depression Severity Prediction Based on Correlation among Audio Features Using a Graph Convolutional Neural Network. [PDF]

Diagnostics (Basel), 2023
Ishimaru M, Okada Y, Uchiyama R, Horiguchi R, Toyoshima I.   +4 more
europepmc   +1 more source

Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics

Advanced Intelligent Discovery, EarlyView.
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong, Zijian Huang, Xuanwu Wang, Zhenghui Feng, Wei Liu, Bingding Huang   +5 more
wiley   +1 more source

Schizophrenia recognition based on three-dimensional adaptive graph convolutional neural network. [PDF]

Sci Rep
Yin G, Yuan J, Chen Y, Guo G, Shi D, Wang L, Zhao Z, Zhao Y, Zhang M, Dong Y, Wang B, Tan S.   +11 more
europepmc   +1 more source

A survey of field programmable gate array (FPGA)-based graph convolutional neural network accelerators: challenges and opportunities. [PDF]

PeerJ Comput Sci, 2022
Li S, Tao Y, Tang E, Xie T, Chen R.
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

A fused biometrics information graph convolutional neural network for effective classification of patellofemoral pain syndrome. [PDF]

Front Neurosci, 2022
Xiong B, OuYang Y, Chang Y, Mao G, Du M, Liu B, Xu Y.   +6 more
europepmc   +1 more source

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source

[Research on fatigue recognition based on graph convolutional neural network and electroencephalogram signals]. [PDF]

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi
Li S, Fu Y, Zhang Y, Lu G.
europepmc   +1 more source