Results 111 to 120 of about 50,503 (261)

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

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

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

Anisotropic NMR as a Crucial Tool for Differentiation of Epimers With High Conformational Flexibility

Angewandte Chemie, EarlyView.
Epimer discrimination remains challenging due to subtle NMR differences. Here, we propose a methodology based on 13C‐RCSA and RDC anisotropic parameters, enabling the assignment of two flexible tetraprenyltoluquinol epimers (1a and 1b) with remote stereoclusters.
Juan Carlos C. Fuentes‐Monteverde, Abel M. Forero, Nilamoni Nath, Antonio Hernández Daranas, Carlos Jiménez, Jaime Rodríguez, Christian Griesinger   +6 more
wiley   +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

Bayesian Age-Period-Cohort Modeling and Prediction - BAMP

Journal of Statistical Software, 2007
The software package BAMP provides a method of analyzing incidence or mortality data on the Lexis diagram, using a Bayesian version of an age-period-cohort model. A hierarchical model is assumed with a binomial model in the first-stage.
Volker J. Schmid, Leonhard Held
doaj  

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

Evaluating diabetes risk: Bayesian hierarchical models and machine learning integration

Heliyon
Type 2 diabetes mellitus (T2DM) is a global health concern influenced by factors such as obesity, sedentary behavior, and poor diet. This study aims to analyze both regional and individual predictors of T2DM in Bangladesh by integrating Bayesian ...
Noor Muhammad Khan, Md Mizanoor Rahman, Mashfiqul Huq Chowdhury   +2 more
doaj   +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

fishStan: Hierarchical Bayesian models for fisheries

Journal of Open Source Software, 2022
Richard A. Erickson, Daniel S. Stich, Jillian L. Hebert   +2 more
openaire   +1 more source