Results 191 to 200 of about 1,429,068 (340)

An Explainable Multimodal Artificial Intelligence Model Integrating Histopathological Microenvironment and EHR Phenotypes for Germline Genetic Testing in Breast Cancer

Advanced Science, EarlyView.
This study presents an explainable multimodal AI model, MAIGGT (Multimodal Artificial Intelligence Germline Genetic Testing), that combines whole‐slide histopathology with clinical data for accurate germline BRCA1/2 mutation prescreening. By integrating digital pathology and EHR phenotypes, MAIGCT enables cost‐effective, scalable hereditary breast ...
Zijian Yang, Changyuan Guo, Jiayi Li, Yalun Li, Lei Zhong, Pengming Pu, Tongxuan Shang, Lin Cong, Yongxin Zhou, Guangdong Qiao, Ziqi Jia, Hengyi Xu, Heng Cao, Yansong Huang, Tianyi Liu, Jian Liang, Jiang Wu, Dongxu Ma, Yuchen Liu, Ruijie Zhou, Xiang Wang, Jianming Ying, Meng Zhou, Jiaqi Liu   +23 more
wiley   +1 more source

Deep learning-based spatial-temporal graph neural networks for price movement classification in crude oil and precious metal markets

Machine Learning with Applications
In this study, we adapt three spatial-temporal graph neural network models to the unique characteristics of crude oil, gold, and silver markets for forecasting purposes.
Parisa Foroutan, Salim Lahmiri
doaj  

SA-UMamba: Spatial attention convolutional neural networks for medical image segmentation. [PDF]

PLoS One
Liu L, Huang Z, Wang S, Wang J, Liu B.
europepmc   +1 more source

ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs

, 2015
Wenpeng Yin, Hinrich Schütze, Bing Xiang, Bowen Zhou   +3 more
openalex   +2 more sources

Part Detector Discovery in Deep Convolutional Neural Networks [PDF]

, 2015
Marcel Simon, Erik Rodner, Joachim Denzler   +2 more
openalex   +1 more source

Deciphering Cell Type Abundance in Proteomics Data Through Graph Neural Networks

Advanced Science, EarlyView.
GraphDEC constructs pseudo bulk reference data using a mixup strategy, then learns low dimensional representations via autoencoders to model intra and inter dataset relationships. These are processed through a multi view adaptive neighborhood aware GNN, integrating reference, target, and joint graphs.
Zhiming Dai, Yujie Song, Tuoshi Qi, Hongyu Zhang, Huiying Zhao, Zheng Wang, Yuedong Yang, Yuansong Zeng   +7 more
wiley   +1 more source

Representing Born effective charges with equivariant graph convolutional neural networks. [PDF]

Sci Rep
Kutana A, Shimizu K, Watanabe S, Asahi R.   +3 more
europepmc   +1 more source

Instant Learning: Parallel Deep Neural Networks and Convolutional Bootstrapping

, 2015
Andrew Simpson
openalex   +2 more sources

deepTFBS: Improving within‐ and Cross‐Species Prediction of Transcription Factor Binding Using Deep Multi‐Task and Transfer Learning

Advanced Science, EarlyView.
DeepTFBS leverages deep learning to predict transcription factor binding sites across species, integrating multi‐task and transfer learning approaches to improve performance in data‐scarce scenarios. This study demonstrates enhanced accuracy in intra‐ and cross‐species prediction, revealing conserved regulatory patterns and functional variants.
Jingjing Zhai, Yuzhou Zhang, Chujun Zhang, Xiaotong Yin, Minggui Song, Chenglong Tang, Pengjun Ding, Zenglin Li, Chuang Ma   +8 more
wiley   +1 more source

Author Correction: Photonic neuromorphic accelerator for convolutional neural networks based on an integrated reconfigurable mesh. [PDF]

Commun Eng
Tsirigotis A, Sarantoglou G, Deligiannidis S, Sánchez E, Sanchez D, Gutierrez A, Bogris A, Capmany J, Mesaritakis C.   +8 more
europepmc   +1 more source