Results 231 to 240 of about 118,631 (311)
Advanced Intelligent Discovery, EarlyView.Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova +4 more
wiley +1 more source
Sensitivity of Bayesian Networks to Noise in Their Parameters. [PDF]
Entropy (Basel)Onisko A, Druzdzel MJ.
europepmc +1 more source
Using data-driven Bayesian networks to analyze maritime accidents in congested waters
Takuya SAMESHIMA
openalex +1 more source
Eliciting Bayesian networks via online surveys: a new approach to knowledge elicitation
, 2017 Peter Edgar Serwylo
openalex +2 more sources
Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning
Advanced Intelligent Discovery, EarlyView.This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley +1 more source
Leveraging Deep Learning, Grid Search, and Bayesian Networks to Predict Distant Recurrence of Breast Cancer. [PDF]
Cancers (Basel)Jiang X, Zhou Y, Wells A, Brufsky A.
europepmc +1 more source
Multi-dimensional Bayesian Network Classifiers
, 2006 Linda C. van der Gaag, Peter R. de Waal
openalex +1 more source
Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics
Advanced Intelligent Discovery, EarlyView.Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong +5 more
wiley +1 more source
Forecasting Subjective Cognitive Decline: AI Approach Using Dynamic Bayesian Networks. [PDF]
J Med Internet ResEtholén A +7 more
europepmc +1 more source
Advanced Intelligent Discovery, EarlyView.Factorization machine with iterative quantum reverse annealing (FMIRA) leverages quantum reverse annealing to perform batch black‐box optimization. Factorization machine with quantum annealing (FMQA) is a widely used python package for solving black‐box optimization problems using D‐Wave quantum annealers.
Andrejs Tučs, Ryo Tamura, Koji Tsuda
wiley +1 more source