Results 221 to 230 of about 3,102,730 (292)

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

Using Dynamic Causal Bayesian Networks to Assess the Role of Patient-Centered Care and Individual-Level Barriers on Viral Suppression Changes Among a Cohort of People with HIV. [PDF]

AIDS Behav
Trepka MJ, Gonzales S, Ward MK, Fennie KP, Sheehan DM, Jean-Gilles M, Devieux J, Gray A, Ladner R, Yoo C.   +9 more
europepmc   +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

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

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

Forecasting Subjective Cognitive Decline: AI Approach Using Dynamic Bayesian Networks. [PDF]

J Med Internet Res
Etholén A, Roos T, Hänninen M, Bouri I, Kulmala J, Rahkonen O, Kouvonen A, Lallukka T.   +7 more
europepmc   +1 more source

AI‐Guided Co‐Optimization of Advanced Field‐Effect Transistors: Bridging Material, Device, and Fabrication Design

Advanced Intelligent Discovery, EarlyView.
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, Shauryaraj K. Jilkar, Aditya Udgaonkar, Yu (Joe) Lu, Ali Mesbah   +4 more
wiley   +1 more source

Bayesian networks to estimate prognosis in vascular cognitive impairment and small vessel disease: integrated analyses of interdependent contributors to multiple outcomes

Overmars LM, Allaart C, Bron EE, Brunner La Rocca H, de Bresser J, Muller M, van Osch MJ, Teunissen C, Tijms BM, Wolters FJ, Biessels GJ.   +10 more
europepmc   +1 more source

Assessing Credibility in Bayesian Networks Structure Learning. [PDF]

Entropy (Basel)
Barth V, Serrão F, Maciel C.
europepmc   +1 more source

Autonomous AI‐Driven Design for Skin Product Formulations

Advanced Intelligent Discovery, EarlyView.
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, Yongrui Xiao, Chunlin Chen, Dimitrios Tsaoulidis, Kai Yang, Tao Chen   +5 more
wiley   +1 more source