Results 141 to 150 of about 11,658 (212)
Glomerular filtration rate estimation in nephrotic patients: creatinine and/or cystatin-C-based equations, or none of them? [PDF]
Carrara F +12 more
europepmc +1 more source
This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath +4 more
wiley +1 more source
Computational and multi-omics systems biology for precision microbiome therapeutics. [PDF]
Dewan A, Mascellino MT.
europepmc +1 more source
Autonomous AI‐Driven Design for Skin Product Formulations
This review presents a comprehensive closed‐loop framework for autonomous skin product formulation design. By integrating artificial intelligence‐driven experiment selection with automated multi‐tiered assays, the approach shifts development from trial‐and‐error to intelligent optimisation.
Yu Zhang +5 more
wiley +1 more source
Multiply-robust estimator of cumulative incidence function difference for right-censored competing risks data. [PDF]
Tian Y, Wu Y.
europepmc +1 more source
A machine learning framework simultaneously predicts four critical properties of monomers for emulsion polymerization: propagation rate constant, reactivity ratios, glass transition temperature, and water solubility. These tools can be used to systematically identify viable bio‐based monomer pairs as replacements for conventional formulations, with ...
Kiarash Farajzadehahary +1 more
wiley +1 more source
CCNA2 orchestrates the PI3K/AKT signaling axis to propel prostate cancer metastasis. [PDF]
Sun J +5 more
europepmc +1 more source
AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley +1 more source
Multi-criteria-based determination of optimal probability distribution with four parameter estimation methods using wind speed data from ten tropical sites in the South Pacific. [PDF]
Ilyas R, Ahmed MR, Khan MGM.
europepmc +1 more source
An explainable CatBoost model was trained to predict the bandgaps of 474 phosphate crystals based on composition and density descriptors. SHAP analysis identified two key variables—d‐electron‐count dispersion and atomic‐density dispersion—as the primary drivers of the model's predictions.
Wenhu Wang +3 more
wiley +1 more source

