Results 231 to 240 of about 549,710 (321)

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

open access: yesAdvanced 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   +4 more
wiley   +1 more source

SuperResNET: Model‐Free Single‐Molecule Network Analysis Software Achieves Molecular Resolution of Nup96

open access: yesAdvanced Intelligent Systems, Volume 7, Issue 3, March 2025.
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]

open access: yesEur J Psychotraumatol, 2017
van de Schoot R, Schalken N, Olff M.
europepmc   +1 more source

Polymerase Chain Reaction. Perturbation Theory and Machine Learning Artificial Intelligence‐Experimental Microbiome Analysis: Applications to Ancient DNA and Tree Soil Metagenomics Cases of Study

open access: yesAdvanced Intelligent Systems, EarlyView.
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

Predicting Postresection Colorectal Liver Metastases Recurrence Using Advanced Graph Neural Networks with Explainability and Causal Inference

open access: yesAdvanced Intelligent Systems, EarlyView.
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

open access: yesAdvanced Intelligent Systems, EarlyView.
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

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