Results 231 to 240 of about 549,710 (321)
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 +4 more
wiley +1 more source
SuperResNET is a powerful integrated software that reconstructs network architecture and molecular distribution of subcellular structures from single molecule localization microscopy datasets. SuperResNET segments the nuclear pore complex and corners, extracts size, shape, and network features of all segmented nuclear pores and uses modularity analysis
Yahongyang Lydia Li +6 more
wiley +1 more source
Systematic search of Bayesian statistics in the field of psychotraumatology. [PDF]
van de Schoot R, Schalken N, Olff M.
europepmc +1 more source
J1210102 A method for formulation Kansei index using QFD and Bayesian statistics
Takuya Kobayashi, Hideyoshi YANAGISAWA
openalex +1 more source
The polymerase chain reaction (PCR).Perturbation Theory and Machine Learning framework integrates perturbation theory and machine learning to classify genetic sequences, distinguishing ancient DNA from modern controls and predicting tree health from soil metagenomic data.
Jose L. Rodriguez +19 more
wiley +1 more source
Hierarchical Bayesian Inverse Problems: A High-Dimensional Statistics Viewpoint [PDF]
Daniel Sanz-Alonso, Nathan Waniorek
openalex +1 more source
This study introduces a framework that combines graph neural networks with causal inference to forecast recurrence and uncover the clinical and pathological factors driving it. It further provides interpretability, validates risk factors via counterfactual and interventional analyses, and offers evidence‐based insights for treatment planning ...
Jubair Ahmed +3 more
wiley +1 more source
Elastic Fast Marching Learning from Demonstration
This article presents Elastic Fast Marching Learning (EFML), a novel approach for learning from demonstration that combines velocity‐based planning with elastic optimization. EFML enables smooth, precise, and adaptable robot trajectories in both position and orientation spaces.
Adrian Prados +3 more
wiley +1 more source
Estimating epidemiological parameters of a stochastic differential model of HIV dynamics using hierarchical Bayesian statistics. [PDF]
Dale R, Guo B.
europepmc +1 more source
Bayesian inference of general noise-model parameters from the syndrome statistics of surface codes [PDF]
Takumi Kobori, Synge Todo
openalex +1 more source

