Results 81 to 90 of about 149,463 (272)

AutomataGPT: Transformer‐Based Forecasting and Ruleset Inference for Two‐Dimensional Cellular Automata

Advanced Science, EarlyView.
We introduce AutomataGPT, a generative pretrained transformer (GPT) trained on synthetic spatiotemporal data from 2D cellular automata to learn symbolic rules. Demonstrating strong performance on both forward and inverse tasks, AutomataGPT establishes a scalable, domain‐agnostic framework for interpretable modeling, paving the way for future ...
Jaime A. Berkovich, Noah S. David, Markus J. Buehler   +2 more
wiley   +1 more source

Limitless stability for Graph Convolutional Networks

CoRR, 2023
This work establishes rigorous, novel and widely applicable stability guarantees and transferability bounds for graph convolutional networks -- without reference to any underlying limit object or statistical distribution. Crucially, utilized graph-shift operators (GSOs) are not necessarily assumed to be normal, allowing for the treatment of networks on
openaire   +3 more sources

De Novo Multi‐Mechanism Antimicrobial Peptide Design via Multimodal Deep Learning

Advanced Science, EarlyView.
Current AI‐driven peptide discovery often overlooks complex structural data. This study presents M3‐CAD, a generative pipeline that leverages 3D voxel coloring and a massive database of over 12 000 peptides to capture nuanced physicochemical contexts.
Xiaojuan Li, Haifan Gong, Yue Wang, Yinuo Zhao, Lixiang Li, Peijing Bao, Qingzhou Kong, Jialu Fu, Boyao Wan, Yumeng Zhang, Jinghui Zhang, Jiekun Ni, Zhongxue Han, Xueping Nan, Kunping Ju, Longfei Sun, Yuerui Ma, Huijun Chang, Mengqi Zheng, Yanbo Yu, Xiaoyun Yang, Xiuli Zuo, Haina Wang, Yanqing Li   +23 more
wiley   +1 more source

A Network Scanning Organization Discovery Method Based on Graph Convolutional Neural Network

Information
With the quick development of network technology, the number of active IoT devices is growing rapidly. Numerous network scanning organizations have emerged to scan and detect network assets around the clock.
Pengfei Xue, Luhan Dong, Chenyang Wang, Cheng Huang, Jie Wang   +4 more
doaj   +1 more source

Causal Prediction of TP53 Variant Pathogenicity Using a Perturbation‐Informed Protein Language Model

Advanced Science, EarlyView.
A TP53‐specific predictor, CaVepP53, is developed by fine‐tuning ESMC on experimentally validated variants, quantifying pathogenicity via Euclidean distances. It outperforms general‐purpose models and extends to five cancer genes, enabling interpretable variant classification for precision medicine.
Huiying Chen, Yang Zhao, Boqiang Hu, Wuke Wang, Minfang Song, Annabeth Xinyu Zhao, Xiangyang Li, Gefei Wang, Yanfen Wang, Weiyan Zheng, Xinpeng Zhang, Xia Lin, Yanbin Yin, Xingxu Huang, Jinfang Zheng, Tingbo Liang   +15 more
wiley   +1 more source

A Deep Graph Structured Clustering Network

IEEE Access, 2020
Graph clustering is a fundamental task in data analysis and has attracted considerable attention in recommendation systems, mapping knowledge domain, and biological science. Because graph convolution is very effective in combining the feature information
Xunkai Li, Youpeng Hu, Yaoqi Sun, Ji Hu, Jiyong Zhang, Meixia Qu   +5 more
doaj   +1 more source

SKOOTS: Skeleton‐Oriented Object Segmentation for Mitochondria in High‐Resolution Cochlear EM Datasets

Advanced Science, EarlyView.
Skeleton‐oriented object segmentation (SKOOTS) introduces a new strategy for 3D mitochondrial instance segmentation by predicting explicit skeletons rather than relying on boundary cues. This approach enables robust analysis of densely packed organelles in large FIB‐SEM datasets.
Christopher J. Buswinka, Richard T. Osgood, Hidetomi Nitta, Artur A. Indzhykulian   +3 more
wiley   +1 more source

Detecting health misinformation: A comparative analysis of machine learning and graph convolutional networks in classification tasks

MethodsX
In the digital age, the proliferation of health-related information online has heightened the risk of misinformation, posing substantial threats to public well-being.
Bharti Khemani, Shruti Patil, Ketan Kotecha, Deepali Vora   +3 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

Long‐Tea‐CLIP: An Expert‐Level Multimodal AI Framework for Fine‐Grained Green Tea Grading Across Five Sensory Dimensions

Advanced Science, EarlyView.
Long‐Tea‐CLIP (Contrastive Language‐Image Pre‐training) presents a multimodal AI framework that integrates visual, metabolomic, and sensory knowledge to grade green tea across appearance, soup color, aroma, taste, and infused leaf. By combining expert‐guided modeling with CLIP‐supervised learning, the system delivers fine‐grained quality evaluation and
Yanqun Xu, Zhengfang Xue, Qing Luo, Xu Liu, Yongquan Xu, Jing Huang, Xingcai Zhang, Xinyuan Zhang, Zhonghua Liu, Zisheng Luo   +9 more
wiley   +1 more source