Results 101 to 110 of about 35,453 (267)

Topology‐Aware Deep Learning on Higher‐Order Structures for Drug Response Prediction

Advanced Science, EarlyView.
We present TopDr, a topology‐aware deep learning framework that encodes both drugs and cell lines as multiscale simplicial complexes, capturing interactions at the 0‐, 1‐, and 2‐simplex levels. By jointly integrating local higher‐order neighborhoods and global topological structures, TopDr generates enriched representations for sensitivity prediction ...
Cong Shen, Guancen Lin, Chuan‐Shen Hu, Yong Wang   +3 more
wiley   +1 more source

De Novo Design of Membrane‐Targeting Antimicrobial Peptides Against Gram‐Negative Bacteria Using a Generative Artificial Intelligence Framework

Advanced Science, EarlyView.
Antimicrobial resistance caused by Gram‐negative bacteria remains difficult to overcome due to the protective outer membrane. To address this challenge, a multi‐condition constrained generative AI framework, GenMTAMP is proposed for de novo membrane‐targeting antimicrobial peptide design by integrating physicochemical and spatial structure descriptors.
Jingxiao Yu, Da‐Wen Sun, QingYi Wei, Yibing Zhang, Jidong Tang, Hongbin Pu   +5 more
wiley   +1 more source

Difference-attention graph convolutional network for skeleton-based gesture recognition

Journal of King Saud University: Computer and Information Sciences
Graph Convolutional Networks (GCNs) have been widely applied to skeleton-based gesture recognition tasks and have achieved remarkable performance. The currently proposed dynamic and topology-non-shared graph convolutional networks outperform conventional
Yadong Wang, Mingxin Yu, Ning Li, Eryang Gao, Guofang Wang, Lianqing Zhu   +5 more
doaj   +1 more source

Ensemble Strategies in Graph Convolutional Networks

IEEE Access
Graph Convolutional Networks (GCNs) are widely used for node classification because they combine node features and graph topology effectively. However, their performance can be limited by structural noise, over smoothing, and sensitivity to graph ...
Rini Widiastuti, Aji Prasetya Wibawa, Wahyu Sakti Gunawan Irianto, Jehad A. H. Hammad   +3 more
doaj   +1 more source

ProSiteHunter: A Unified Framework for Sequence‐Based Prediction of Protein‐Nucleic Acid and Protein‐Protein Binding Sites

Advanced Science, EarlyView.
This study proposed a unified sequence‐based framework for protein binding site prediction, which adopted a tri‐track semantic multi‐source feature fusion strategy to effectively capture diverse macromolecular interaction sites and further improved the accuracy of antibody‐antigen interaction prediction.
Dongliang Hou, Qihang Zhen, Zexin Lv, Xinyue Cui, Suhui Wang, Minghua Hou, Zhan Zhou, Xiaogen Zhou, Guijun Zhang   +8 more
wiley   +1 more source

Construction of Sabatier Volcanoes for CO2 Hydrogenation to C1‐2 Oxygenates Using Data‐Efficient Machine Learning

Advanced Science, EarlyView.
A new data‐efficient framework combining DFT calculations, a neural network model, and automated graph analysis of catalytic reaction networks is proposed and applied to CO2 hydrogenation on transition metal nanoparticles. The analysis shows how efficient C2 oxygenate production requires a balance between CHx formation, C–C coupling, protonation, and ...
Mikhail V. Polynski, Sergey M. Kozlov
wiley   +1 more source

Non-convolutional graph neural networks.

Advances in Neural Information Processing Systems 37
Rethink convolution-based graph neural networks (GNN) -- they characteristically suffer from limited expressiveness, over-smoothing, and over-squashing, and require specialized sparse kernels for efficient computation. Here, we design a simple graph learning module entirely free of convolution operators, coined random walk with unifying memory (RUM ...
Yuanqing Wang, Kyunghyun Cho
openaire   +3 more sources

Accurately Deciphering Tissue Heterogeneity From Spatial Multi‐Modal and Multi‐Omics With STransformer

Advanced Science, EarlyView.
STransformer is a unified deep learning framework designed to seamlessly accommodate a comprehensive landscape of spatial data. By simultaneously capturing short‐range cellular interactions and tissue‐wide semantic patterns, it extracts robust representations to accurately dissect complex tissue heterogeneity.
Xingyi Li, Jialuo Xu, Gaoyuan Du, Xiangting Jia, Dongmin Zhao, Chunyan Zhou, Kexin Xiao, Jia Gu, Junnan Zhu, Xuequn Shang   +9 more
wiley   +1 more source

Inverse link prediction with graph convolutional networks for knowledge-preserving sparsification in cheminformatics

Journal of Big Data
Large-scale cheminformatics datasets, such as those used in drug discovery and materials science, are often represented as dense similarity graphs; however, their complexity hinders scalable analysis and interpretability.
Elnaz Bangian Tabrizi, Mehrdad Jalali, Mahboobeh Houshmand   +2 more
doaj   +1 more source

Advancing Link Prediction with a Hybrid Graph Neural Network Approach

Mathematics
Social media platforms produce extensive user–item interaction data that demand advanced analytical models for effective personalization. This study investigates the link prediction task within social recommendation systems using Graph Neural Networks ...
Siwar Gharsallah, Samah Yahia, Wided Bouchelligua, Tahani Bouchrika   +3 more
doaj   +1 more source