Results 131 to 140 of about 37,604 (258)

Research on a multimodal emotion perception model based on GCN+GIN hybrid model

Discover Applied Sciences
Graph neural networks (GNNs) have demonstrated strong performance in handling graph-structured data in recent years, particularly in capturing complex inter-node relationships among data samples, showcasing advantages over traditional neural networks ...
Yingqiang Wang, Elcid A. Serrano
doaj   +1 more source

Using graph convolutional neural networks to learn a representation for glycans. [PDF]

Cell Rep, 2021
Burkholz R, Quackenbush J, Bojar D.
europepmc   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

De novo spatiotemporal modelling of cell-type signatures in the developmental human heart using graph convolutional neural networks. [PDF]

PLoS Comput Biol, 2022
Marco Salas S, Yuan X, Sylven C, Nilsson M, Wählby C, Partel G.   +5 more
europepmc   +1 more source

SigmaFormer: Augmenting transformer encoders with COSMO sigma profiles for pure component property prediction

AIChE Journal, EarlyView.
Abstract Transformer‐based molecular models pretrained on SMILES strings demonstrate strong performance in property prediction. However, these model often lack explicit integration of molecular surface charge distributions that govern intermolecular interactions such as hydrogen bonding and polarity.
Tae Hyun Kim, Silabrata Pahari, Joseph Sang‐Il Kwon   +2 more
wiley   +1 more source

Novel Solubility Prediction Models: Molecular Fingerprints and Physicochemical Features vs Graph Convolutional Neural Networks. [PDF]

ACS Omega, 2022
Lee S, Lee M, Gyak KW, Kim SD, Kim MJ, Min K.   +5 more
europepmc   +1 more source

Harnessing Large Language Models to Advance Microbiome Research: From Sequence Analysis to Clinical Applications

Advanced Intelligent Discovery, EarlyView.
Large language models are transforming microbiome research by enabling advanced sequence profiling, functional prediction, and association mining across complex datasets. They automate microbial classification and disease‐state recognition, improving cross‐study integration and clinical diagnostics.
Jieqi Xing, He Wang, Yangyang Sun, Xiaoquan Su, Shunyao Wu   +4 more
wiley   +1 more source

Convolutional Kernel Networks for Graph-Structured Data

, 2020
International audienceWe introduce a family of multilayer graph kernels and establish new links between graph convolutional neural networks and kernel methods.
Chen, Dexiong, Mairal, Julien, Jacob, Laurent   +2 more
core   +1 more source

Link Performance Prediction of Power Fiber Optic Communication System Based on Attention Mechanism and Convolutional Neural Network Fusion

Electrica
In order to enhance the multi-objective optimization capability of power communication transmission networks, the author proposes an optimization method that integrates improved graph neural networks (GNNs) and genetic algorithms (GAs).
Yong Zhang, Yan Liu, Chunying Wang, Jiaojiao Dong   +3 more
doaj   +1 more source

Automated freezing of gait assessment with marker-based motion capture and multi-stage spatial-temporal graph convolutional neural networks. [PDF]

J Neuroeng Rehabil, 2022
Filtjens B, Ginis P, Nieuwboer A, Slaets P, Vanrumste B.   +4 more
europepmc   +1 more source