Results 81 to 90 of about 38,896 (265)

Cross‐Modal Denoising and Integration of Spatial Multi‐Omics Data with CANDIES

Advanced Science, EarlyView.
In this paper, we introduce CANDIES, which leverages a conditional diffusion model and contrastive learning to effectively denoise and integrate spatial multi‐omics data. We conduct extensive evaluations on diverse synthetic and real datasets, CANDIES shows superior performance on various downstream tasks, including denoising, spatial domain ...
Ye Liu, Wanpeng Zou, Yuekai Li, Jiayi Wang, Mingxuan Cai, Hongmin Cai   +5 more
wiley   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

Advanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin, Qian Lei, Wei Yan, Yue Li, Tong Xie, Shuang Zhou, Chong Wang, Bram Hoex, Qiang Long, Caoyang Jiang, Min Song, Zhou Li, Zhangwei Wang   +12 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

Efficient Screening of Organic Singlet Fission Molecules Using Graph Neural Networks

Advanced Science, EarlyView.
A high‐throughput screening framework based on graph neural networks (GNNs) and multi‐level validation facilitates the identification of singlet fission (SF) candidates. By efficiently predicting excitation energies across 20 million molecules, and integrating TDDFT calculations, synthetic accessibility assessments, and GW+BSE calculations, this ...
Li Fu, Longfei Lv, Fan Zhang, Si Zhou, Weiwei Gao, Jijun Zhao   +5 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

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

CMOS‐Integrated Synaptic Photoreceptor Chip Inspired by Insect Visual Processing

Advanced Science, EarlyView.
CMOS‐integrated Si QDs/ReS2 synaptic photoreceptor array mimics the parallel processing and wavelength‐selective strategy of insect vision. By combining intrinsic ultraviolet‐violet sensitivity with synaptic plasticity, the chip enables frontend sensory redundancy reduction without external filters, offering a scalable pathway toward lowpower ...
Jian Chai, Xinyi Xu, Yue Wang, Xiaochen Wang, Hailiang Wang, Yunfei Xie, Xinwei Zhang, Haoyu Wang, Hao Ning, Jiangming Lin, Yongliang Xie, Qihai Jiang, Baoshi Qiao, Xiaolei Ding, Luyao Ma, Shukai Duan, Zhenyi Ni, Huan Hu, Xin He, Fei Xue, Feichi Zhou, Lingfei Li, Srikrishna Chanakya Bodepudi, Rui Yuan, Bin Yu, Yang Xu   +25 more
wiley   +1 more source

From Spectral Graph Convolutions to Large Scale Graph Convolutional Networks

CoRR, 2022
Graph Convolutional Networks (GCNs) have been shown to be a powerful concept that has been successfully applied to a large variety of tasks across many domains over the past years. In this work we study the theory that paved the way to the definition of GCN, including related parts of classical graph theory.
openaire   +2 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