Results 81 to 90 of about 39,156 (266)

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

Multi-channel based edge-learning graph convolutional network

Dianxin kexue, 2022
Usually the edges of the graph contain important information of the graph.However, most of deep learning models for graph learning, such as graph convolutional network (GCN) and graph attention network (GAT), do not fully utilize the characteristics of ...
Shuai YANG, Ruiqin WANG, Hui MA
doaj   +2 more sources

Cold-Start Traffic State Forecasting at Unseen Sensor Locations via Support-Conditioned Meta-Graph Learning

Sensors
Traffic sensors provide real-time measurements of current traffic conditions, whereas traffic management applications require forecasts of future traffic speed or flow.
Can Wang, Zhiyu Wang, Weijie Wang, Jing Gan, Yanni Ju   +4 more
doaj   +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

A malware classification method based on directed API call relationships.

PLoS ONE
In response to the growing complexity of network threats, researchers are increasingly turning to machine learning and deep learning techniques to develop advanced models for malware detection.
Cuihua Ma, Zhenwan Li, Haixia Long, Anas Bilal, Xiaowen Liu   +4 more
doaj   +1 more source

Improved GCN Model for Inexact Graph Matching

Jisuanji kexue yu tansuo, 2020
Aiming at the problem of mining the features of topology nodes deficiently in the existing inexact graph matching, this paper proposes an improved graph convolutional network (GCN) model for inexact graph matching. Firstly, considering that the selecting
LI Changhua, CUI Liyang, LI Zhijie
doaj   +1 more source

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

Simple and Deep Graph Convolutional Networks

CoRR, 2020
Graph convolutional networks (GCNs) are a powerful deep learning approach for graph-structured data. Recently, GCNs and subsequent variants have shown superior performance in various application areas on real-world datasets. Despite their success, most of the current GCN models are shallow, due to the {\em over-smoothing} problem.
Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, Yaliang Li   +4 more
openaire   +3 more sources

Drug-induced liver injury prediction based on graph convolutional networks and toxicogenomics.

PLoS Computational Biology
Drug-induced liver injury is a leading cause of high attrition rates for both candidate drugs and marketed medications. Previous in silico models may not effectively utilize biological drug property information and often lack robust model validation.
Tong Xiao, Ying Liu, Kaimiao Hu, Kaimin Guo, Mengying Zhang, TingTing Wang, Weihua Lei, Wenjia Wang, Shuiping Zhou, Yunhui Hu, Ran Su   +10 more
doaj   +1 more source

Synergistic Integration of Artificial Merkel Disc and Meissner Corpuscle via Dermal Papillary Structures for Mechanically Filtered Multimodal Tactile Sensing

Advanced Science, EarlyView.
A multimodal tactile sensor module that mimics the spatial arrangement and function of Merkel discs and Meissner corpuscles within the human papillary structure operates in a self‐powered manner, responding to both dynamic and static stimuli, achieving tactile perception more similar to human skin.
Jaehyeong Kim, Bohee Maeng, Seunghwan Seo, Kyoung‐Yong Chun, Chang‐Soo Han   +4 more
wiley   +1 more source