Results 181 to 190 of about 5,636 (303)

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

Graph Convolutional Networks Enable Fast Hemorrhagic Stroke Monitoring With Electrical Impedance Tomography. [PDF]

IEEE Trans Biomed Eng
Toivanen J, Kolehmainen V, Paldanius A, Hanninen A, Hauptmann A, Hamilton SJ.   +5 more
europepmc   +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

Predicting the Unpredictable: AI-Driven Prognosis in Pancreatic Neuroendocrine Neoplasms. [PDF]

Cancers (Basel)
Merola E, Pirino E, Marcucci S, Chierichetti F, Michielan A, Bernardoni L, Gabbrielli A, Dore MP, Fanciulli G, Brolese A.   +9 more
europepmc   +1 more source

Field‐Free Spin‐Splitting‐Torque Driven Stochastic Neuron Mimicking the Neuromorphic Imagination for High‐Performance Recognition

Advanced Science, EarlyView.
The human brain's imagination, which enables autonomous driving hazard avoidance by completing missing visual information, relies on Gaussian‐stochastic neuron. We report the altermagnetic RuO2 spintronic neurons integrating field‐free switching and intrinsic Gaussian stochasticity, building an all‐spin ANN for high‐quality image repairing and high ...
Junwei Zeng, Baoshan Cui, Xi Guo, Jiahao Liu, Xiaoyu Feng, Liang Fang, Li Xi, Xiaolong Fan, Xiaoxi Liu, Yang Guo   +9 more
wiley   +1 more source

Multimodal Deep Learning for Prediction of Progression-Free Survival in Patients with Neuroendocrine Tumors Undergoing ¹⁷⁷Lu-Based Peptide Receptor Radionuclide Therapy. [PDF]

Cancers (Basel)
Baur S, Ruhwedel T, Böke E, Kobus Z, Lishkova G, Wetz C, Amthauer H, Roderburg C, Tacke F, Rogasch JM, Samek W, Jann H, Ma J, Eschrich J.   +13 more
europepmc   +1 more source

dnn: An R Package for Feed Foward, Convolutional, and Recurrent Neural Networks

, 2017
The goal of this project is to provide a simple and adaptable package for the R community in the modeling and training of deep neural networks. The three types of networks supported by the dnn package are feed forward, convolutional, and recurrent neural
Xin, Derrick
core  

Toward Long‐Term Reliable Human‐Machine Interaction: A Flexible, Breathable, and Self‐Powered Pressure Sensor System With Firefighting Validation

Advanced Science, EarlyView.
This work develops a breathable potentiometric self‐powered pressure sensor with good wearability, using a non‐woven fabric substrate and hydrogel electrolyte. The sensor is integrated into a five‐channel smart glove to acquire reliable hand motion signals, which are processed by a CNN for identity verification and smart vehicle control in human ...
Qilong Zhang, Zhao Yao, Jiaxu Liu, Yuxuan Hou, Zhongtao Zhang, Jingwei Xue, Leonid Chernogor, Nam Young Kim, Eun Seong Kim, Yuanyue Li, Yang Li   +10 more
wiley   +1 more source

Application of convolutional neural networks for automated segmentation and classification in esophageal diseases. [PDF]

Front Med (Lausanne)
Pu L, Wang X, Yan S, Zhuang S, He X.
europepmc   +1 more source

Dielectric‐Confinement‐Induced in‐Plane Photoelectric Anisotropy in Isotropic Quasi‐1D γ‐GaS Nanoribbon

Advanced Science, EarlyView.
We investigate the geometry‐governed optoelectronic anisotropy arising from dielectric confinement in quasi‐1D γ‐GaS nanoribbons with intrinsically isotropic atomic structures. Dielectric mismatch between the nanoribbon and its surroundings leads to a general polarization‐dependent photoresponse during near‐field scattering.
Jiawei Jing, Jiamei Chen, Xing Xin, Peisong Peng, Zhihao Wu, Weiheng Zhong, Hanyu Zhang, Wanfu Shen, Jihua Zhang, Yuwei Shan, Yuanzheng Li, Xingang Zhao, Wei Xin, Fang Wang, Weida Hu, Haiyang Xu, Yichun Liu   +16 more
wiley   +1 more source