Results 181 to 190 of about 529,167 (319)

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

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, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

The Bias-and-Expertise Model: A Bayesian Network Model of Political Source Characteristics. [PDF]

Cogn Sci
Young DJ, de-Wit LH.
europepmc   +1 more source

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

Vitamin D Receptor Gene Polymorphisms Differentiated Between Tuberculosis Disease and Infection: Causal Association Study

Infection and Drug Resistance
Jaina Razbek,1 Yanggui Chen,2 Jiandong Yang,2 Yaying Zhang,1 Baofeng Wen,1 Junan Wang,1 Xiaomin Wang,1 Guliziba Kuerbanjiang,1 Abulikemu Aili,1 Mingqin Cao1 1Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical ...
Razbek J, Chen Y, Yang J, Zhang Y, Wen B, Wang J, Wang X, Kuerbanjiang G, Aili A, Cao M   +9 more
doaj  

Study on the driving mechanism of cultivated land change in the urban-rural fringe with Bayesian network modeling. [PDF]

Sci Rep
Wang J, Zhu Z, Chen M, Zhang Y.
europepmc   +1 more source

Diagnostic Bayesian network in building energy systems: Current insights, practical challenges, and future trends

Chujie Lu, Ziao Wang, Martín Mosteiro-Romero, Laure Itard   +3 more
openalex   +1 more source

Challenge of applying ‘subjective interpretation’ with Bayesian network analysis in Art and Culture

, 2019
NGUYỄN Minh Hoàng
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, Zijian Huang, Xuanwu Wang, Zhenghui Feng, Wei Liu, Bingding Huang   +5 more
wiley   +1 more source

Comparative efficacy and safety of antiresorptive and anabolic therapies for male osteoporosis: an updated Bayesian network meta-analysis. [PDF]

Front Endocrinol (Lausanne)
Li Z, Zhang L, Xue C, Lu C, Wang L.
europepmc   +1 more source

Factorization Machine with Iterative Quantum Reverse Annealing: A Python Package for Batch Black‐Box Optimization With Reverse Quantum Annealing

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