Results 171 to 180 of about 1,872,984 (299)
Data‐Driven Design of Self‐Adhesive Epidermal Electrodes and Sensors
This work presents self‐adhesive, stretchable epidermal electrodes and sensors developed through a data‐driven design framework that integrates artificial neural networks with genetic algorithms. By tailoring optimization objectives, either maximizing electrical conductivity and adhesion or enhancing piezoresistive sensitivity, the study enables the ...
Xuan Li +10 more
wiley +1 more source
A CNN Hyperparameters Optimization Based on Particle Swarm Optimization for Mammography Breast Cancer Classification. [PDF]
Aguerchi K +3 more
europepmc +1 more source
Artificial Intelligence‐Guided Phenotypic Drug Repurposing Against Streptococcus pneumoniae
Integrated artificial intelligence (AI) ensembles identify highly potent repurposed drugs against Streptococcus pneumoniae. Prospective virtual screening of 6747 drugs uncovers nine active candidates, including thiostrepton. This drug exhibits low picomolar potency and bypasses established resistance in multidrug‐resistant strains, demonstrating an ...
Joshua S. Fitch +6 more
wiley +1 more source
TSTScope is an interpretable AI framework that integrates single‐cell transcriptomes with TCR information through curated gene‐program constraints. By linking receptor context to functional T cell states, it reveals response‐associated tumor‐specific T cell programs in lung cancer immunotherapy cohorts and defines an MPR score associated with ...
Shiwei Cao +8 more
wiley +1 more source
A precise model for skin cancer diagnosis using hybrid U-Net and improved MobileNet-V3 with hyperparameters optimization. [PDF]
Kumar Lilhore U +8 more
europepmc +1 more source
Automatic hyperparameter tuning of topology optimization algorithms using surrogate optimization
This paper presents a new approach that automates the tuning process in topology optimization of parameters that are traditionally defined by the user. The new method draws inspiration from hyperparameter optimization in machine learning.
Ha, Dat, Carstensen, Josephine
core +1 more source
Technical limitations often let dominant signals overshadow rare cell types and fine‐grained heterogeneity in spatial transcriptomics. SemanticST, a graph neural network using multi‐semantic graph fusion and a novel min‐cut loss, recovers these subtle patterns.
Roxana Zahedi +7 more
wiley +1 more source
Machine Learning of Temperature‐Dependent Chemical Kinetics Using Parallel Droplet Microreactors
An integrated droplet microfluidics and machine learning framework enables high‐throughput characterization of temperature‐dependent reaction kinetics. Time‐resolved measurements from thousands of droplets train Neural ODE models that accurately predict nonlinear reaction dynamics across diverse thermal environments, bridging large‐scale ...
Mamoru Saita, Yutaka Hori
wiley +1 more source
ProMetNet introduces a biologically constrained deep learning framework for proteo‐metabolomic integration by embedding Reactome‐derived pathway topology into neural networks. It captures non‐linear molecular dependencies and pathway‐level metabolic reorganization, enabling interpretable discrimination.
Minghui Zhao +6 more
wiley +1 more source
Loss function influence on hyperparameter optimization for observational healthcare prediction models. [PDF]
Vereijken F +3 more
europepmc +1 more source

