Temporal Convolutional Neural Network Analysis of Magnetocardiography Signals for Detection of Pulmonary Hypertension. [PDF]
Qi Y +5 more
europepmc +1 more source
An Integrated Artificial Intelligence Framework for Nanomedicine Engineering
This review outlines a closed‐loop AI ecosystem for nanomedicine engineering. By synergizing multimodal data harmonization, objective‐driven AI architectures, and automated high‐throughput validation, this “Design‐Make‐Test‐Analyze” (DMTA) workflow transforms traditional heuristic formulation into a predictive, self‐optimizing paradigm, accelerating ...
Meichen Dong +10 more
wiley +1 more source
Vision-based tactile sensing enhanced by microstructures and lightweight convolutional neural network. [PDF]
Shi M, Zhang Y, Guo X, Yeatman EM.
europepmc +1 more source
In this study, we demonstrate that aberrant MEKK3 signaling in endothelial cells and microglia drives the progression of cerebral cavernous malformations (CCMs). Furthermore, we identify DPDH as a potent small‐molecule MEKK3 inhibitor with substantial therapeutic potential for CCM treatment.
Weiwei Zheng +17 more
wiley +1 more source
Frequency-Aware Lightweight Convolutional Neural Network for Edge-Enabled Intelligent Bearing Fault Diagnosis and Health Management. [PDF]
Tian H, Hua S, Gui W, Zou S, Wang C.
europepmc +1 more source
Enhancing Specific Stiffness of Nano‐Architected Materials Through Generative AI and Active Learning
A structure‐centric generative framework searches voxelated design space directly, coupling a variational autoencoder with multi‐objective optimization and active learning to progressively expand the validated design domain. The discovered nano‐architectures, realized as pyrolytic carbon by two‐photon lithography, deliver 18–23% higher stiffness and 26–
Jinwook Yeo +4 more
wiley +1 more source
LiteDriveNet for driver distraction classification using a lightweight multi-scale convolutional neural network. [PDF]
Namachivayam A, Paramanandham N.
europepmc +1 more source
Flexible Integration of Structurally Diverse Spatial Multi‐Omics With AgaeSMO
AgaeSMO is a versatile and robust deep learning framework for flexible cross‐integration of spatial multi‐omics data. Leveraging a GATs network with a graph attention module, it learns joint embedding across diverse modalities, enabling integration of both structurally paired (e.g., spatial transcriptome‐epigenome, transcriptome‐proteome, transcriptome‐
Kunpeng Liu +9 more
wiley +1 more source
Application of a convolutional neural network for automatic cytological diagnosis of 7 common canine tumors on whole slide images: a proof-of-concept. [PDF]
Attini M +4 more
europepmc +1 more source

