Results 91 to 100 of about 37,604 (258)

SpaMode: A Broadly Applicable Framework for Deciphering Spatial Multi‐Omics Using Multimodal Mixture of Disentangled Experts

Advanced Science, EarlyView.
SpaMode introduces a versatile framework for spatial multi‐omics integration across vertical, horizontal, and mosaic scenarios. By disentangling modality‐invariant and variant features through a mixture‐of‐experts mechanism, it adaptively reconfigures spatially heterogeneous signals.
Xubin Zheng, Xinlei Huang, Xiang Zhou, Dian Meng, Ziyue Qiao, Zhiyuan Yuan, Lixin Cheng   +6 more
wiley   +1 more source

Graph Convolutional Neural Networks with Negative Sampling [PDF]

, 2022
University of Technology Sydney. Faculty of Engineering and Information Technology.Convolutional neural networks (CNNs) can learn potential features from large amounts of Euclidean data, such as text, images, which produce a satisfactory performance on ...
Duan, Wei
core  

Graph Neural Networks

, 2021
The recent wave of impressive results obtained in fields as varied as computer vision, natural language processing, bioinformatics and many more can be attributed to the advances in training and designing neural networks.
Giulia Fracastoro, Diego Valsesia
core   +1 more source

Combining Spatial Multi‐Omics Data to Decipher Spatial Domains and Elucidate Cell Heterogeneity Based on Self‐Supervised Graph Learning

Advanced Science, EarlyView.
A self‐supervised multi‐view graph fusion framework integrates spatial multi‐omics, excelling in domain identification and denoising. It reconstructs spatial pseudo‐expression, jointly analyzes multi‐omics data, infers RNA velocity, predicts spatial omics features from single‐cell multi‐omics, and detects spatially dark genes and transcription factors,
Yuejing Lu, Rui Qiao, Ying Li, Junhong Li, Nuoya Yue, Jing Ge, Jiao Wang, Luonan Chen, Peiluan Li   +8 more
wiley   +1 more source

Knowledge graph learning algorithm based on deep convolutional networks

Intelligent Systems with Applications
Knowledge graphs (KGs) serve as invaluable tools for organizing and representing structural information, enabling powerful data analysis and retrieval.
Yuzhong Zhou, Zhengping Lin, Jie Lin, Yuliang Yang, Jiahao Shi   +4 more
doaj   +1 more source

A Portable and Dual‐Button Microneedle Device Enables Intelligent Multimodal Laser Sensing

Advanced Science, EarlyView.
A portable and dual‐button microneedle device enables rapid interstitial fluid sampling. Coupled with multimodal laser sensing and AI‐assisted data processing, the platform enables simultaneous molecular and elemental analysis for minimally invasive and multiplexed health assessment toward point‐of‐care diagnostics.
Yuanchao Liu, Xiujuan Hu, Shengqun Shi, Bowen Li, Zhixing Ge, Yunchen Long, Chaochao Sun, Annan Chen, Bingbing Gao, Lianbo Guo, Condon Lau, Wei Luo, Chwee Teck Lim   +12 more
wiley   +1 more source

Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting

, 2020
Modeling complex spatial and temporal correlations in the correlated time series data is indispensable for understanding the traffic dynamics and predicting the future status of an evolving traffic system.
Bai, Lei, Wang, Can, Yao, Lina, Wang, Xianzhi, Li, Can   +4 more
core  

Graph convolutional neural networks for 3D data analysis

, 2023
(English) Deep Learning allows the extraction of complex features directly from raw input data, eliminating the need for hand-crafted features from the classical Machine Learning pipeline.
Mosella Montoro, Albert
core   +1 more source

Decoding Spatial Heterogeneity and Multi‐Omics Regulation with Hierarchical Graph Learning

Advanced Science, EarlyView.
ABSTRACT Recent advances in spatial multi‐omics technologies have enabled the simultaneous profiling of multiple molecular layers within the same tissue slice, providing unprecedented opportunities to investigate tissue spatial organization. However, most existing computational methods identify spatial domains in a purely data‐driven manner, rarely ...
Jiazhou Chen, Jiahui Xie, Yi Liao, Junyu Li, Longqi Liu, Xin Gao, Hongmin Cai   +6 more
wiley   +1 more source

STAID: A Self‐Refining Deep Learning Framework for Spatial Cell‐Type Deconvolution with Biologically Informed Modeling

Advanced Science, EarlyView.
STAID is a unified deep learning framework that couples iterative pseudo‐spot refinement with neural network training through a feedback loop and exploits gene co‐expression information to model higher‐order interactions, achieving accurate and robust cell‐type deconvolution in spatial transcriptomics.
Jixin Liu, Shuli Sun, Zhengliang Lv, Xinyu Liu, Yihua Wang, Bingqiang Liu   +5 more
wiley   +1 more source