Results 161 to 170 of about 7,662,414 (297)

Hierarchical Bayesian adaptive lasso methods on exponential random graph models

Applied Network Science
The analysis of network data has become an increasingly prominent and demanding field across multiple research fields including data science, health, and social sciences, requiring the development of robust models and efficient computational methods. One
Dan Han, Vicki Modisette, Melinda Forthofer, Rajib Paul   +3 more
doaj   +1 more source

fishStan: Hierarchical Bayesian models for fisheries

Journal of Open Source Software, 2022
Richard A. Erickson, Daniel S. Stich, Jillian L. Hebert   +2 more
openaire   +2 more sources

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

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
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

Bayesian analysis of daily maximum ozone levels

, 2012
Ground level ozone is one of the six criteria primary pollutants that is monitored by the United States Environmental Protection Agency. Statistical methods are increasingly being used to model ground level ozone concentration data.
Bakar, K.S., Bakar, Khandoker Shuvo
core   +1 more source

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

Generative and Experimental Validation of High Refractive Index Polymers via Domain Knowledge Approach with Small Data

Advanced Intelligent Discovery, EarlyView.
This research demonstrates that the combination of domain knowledge–based multiple regression, multi‐objective Bayesian optimization, and generative models is a suitable prediction tool for candidates of high refractive index polymers, even with the constraints in the model trained on limited data. The experimental validation can reproduce the proposed
Takuya Yokoo, Seigo Watanabe, Hana Matsunaga, Kenichi Oyaizu   +3 more
wiley   +1 more source

Modeling hierarchical relationships in epidemiological studies: a Bayesian networks approach [PDF]

Hierarchical relationships between risk factors are seldom taken into account in epidemiological studies though some authors stressed the importance of doing so, and proposed a conceptual framework in which each level of the hierarchy is modeled ...
Nguefack-Tsague, Georges, Zucchini, Walter   +1 more
core  

Bayesian Hierarchical Models for the integration of genomic platforms [PDF]

, 2012
phd thesis on bayesian hierarchical models for the integrated analysis of genomic platforms, such as gene expression microarrays and array ...
TRENTINI, FILIPPO
core  

Multicenter Validation of Machine Learning Models for Predicting Myopia Progression Across School‐Based Screening and Optical Intervention Settings in Chinese Children and Adolescents

Advanced Intelligent Discovery, EarlyView.
Machine‐learning models predict myopia trajectories from 12 routine ocular variables in 40 445 children across 10 hospitals. Externally validated across school‐based screening and optical‐intervention settings, the best models achieve AUROC 0.97 for myopia status and R2 0.83 for spherical equivalent, enabling personalized risk stratification to curb ...
Yitong Zhang, Mingxuan Liu, Yifei Chen, Ying Wu, Lijun Zhang, Mengyao Guo, Yulan Yan, Runmei Wei, Zihan Cui, Peng Luo, Fang Chen, Bo Yang, Youcang Han, Xiaoyang Liu, Qiyuan Tian   +14 more
wiley   +1 more source