Results 121 to 130 of about 7,749,372 (301)
Advanced Science, EarlyView.We use lysine‐to‐glutamine mutations to study the effect of electrostatics on the kinetics and thermodynamics of alpha‐synuclein amyloid fibril formation. We find that mutational effects map on their structural context within fibrils and identify residues that modulate the energy landscape of alpha‐synuclein self‐assembly. Our work outlines a scalable,
Antonin Kunka +10 more
wiley +1 more source
Principled Decision-Making Workflow with Hierarchical Bayesian Models of High-Throughput Dose-Response Measurements. [PDF]
Entropy (Basel), 2021 Ma EJ, Kummer A.
europepmc +1 more source
Hierarchical Bayesian Spatio-Temporal Analysis of Childhood Cancer Trends [PDF]
, 2012 In this paper, generalized additive mixed models are constructed for the analysis of geographical and temporal variability of cancer ratios. In this class of models, spatially correlated random effects and temporal components are adopted. Spatio-temporal
Rosychuk, Rhonda J., Torabi, Mahmoud
core +1 more source
Towards a Compositional Framework for Describing Human Phenotypes
Advanced Science, EarlyView.The Phenotype Assembly Method (PhenoAM) decomposes phenotype variables into measurable Features and typed Qualifiers, enabling standardized, machine‐readable Phenome Data Elements (PhenoDEs) that preserve measurement context. Applied in the International Human Phenome Project (IHPP), the framework yields 58 371 PhenoDEs and supports component‐level ...
Wanting Hu +11 more
wiley +1 more source
Smart Bioinks for 4D Bioprinting: Requirements, Design, and Applications
Advanced Science, EarlyView.Smart bioinks empower 4D‐bioprinted constructs to dynamically adapt and remodel in response to stimuli, effectively biomimicking native tissues. Artificial Intelligence (AI) and Machine Learning (ML) play a guiding role in their rational design by optimizing relevant properties.
Shangsi Chen +11 more
wiley +1 more source
A Bayesian hierarchical model for longtitudinal data [PDF]
, 2010 The paper investigates a Bayesian hierarchical model for the analysis of longitudinal data from a randomized controlled clinical tuberculosis trial. Data for each subject are observed on thirteen time point of occasions of the trial.
Senthilkumar, B +3 more
core +1 more source
Bayesian Learning in Undirected Graphical Models: Approximate MCMC algorithms [PDF]
, 2004 Bayesian learning in undirected graphical models—computing posterior distributions over parameters and predictive quantities—is exceptionally difficult.
Murray, Iain +2 more
core
Comparing families of dynamic causal models [PDF]
, 2010 Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previous applications in the biological sciences have mainly focussed on model selection in which one first selects the model with the highest evidence and ...
Stephan, Klaas E +46 more
core +2 more sources
Functional MXene Inks for Printed Electronics: Chemistry, Rheology, and Devices
Advanced Science, EarlyView.Functional MXene inks are emerging as building blocks for MXetronics, connecting MXene synthesis, surface functionalization, ink formulation, and advanced printing with device‐level applications. This review maps how composition, stability, rheology, and processability govern printed structures and enable high‐performance sensors, energy‐storage ...
Shahzaib Ali +6 more
wiley +1 more source
Bayesian Hierarchical Tobit Models: an application to travel distance analysis
Ingeniería, 2017 The objective of travel distance models is to better understand travel behavior so that policies can be implemented for reducing travel and with that the externalities of transport such as air pollution, congestion, and crashes.
Jonathan Aguero-Valverde
doaj +1 more source