Results 221 to 230 of about 1,210,282 (359)

AI Powered Biobanks From Static Archives to Dynamic Discovery Engines

open access: yesAdvanced Intelligent Discovery, EarlyView.
Large language models (LLMs) provide a potential framework for transforming biobanks from static data repositories into intelligent discovery engines. By enabling unified representation and analysis of multimodal biomedical data, LLM‐based systems facilitate dynamic risk prediction, biomarker identification, and mechanistic interpretation, thereby ...
Wenzhen Yin   +5 more
wiley   +1 more source

Correction: Prevention of spontaneous preterm birth. [PDF]

open access: yesArch Gynecol Obstet
Daskalakis G   +6 more
europepmc   +1 more source

The Interoperability Challenge in DFT Workflows Across Implementations

open access: yesAdvanced Intelligent Discovery, EarlyView.
Interoperability and cross‐validation remain major challenges in the computational materials science. In this work, we introduce a common input/output standard that enables internal translation across multiple workflow managers—AiiDA, PerQueue, Pipeline Pilot, and SimStack—while producing results in a unified schema.
Simon K. Steensen   +13 more
wiley   +1 more source

OXidative Stress PREDictor: A Supervised Learning Approach for Annotating Cellular Oxidative Stress States in Inflammatory Cells

open access: yesAdvanced Intelligent Systems, Volume 7, Issue 3, March 2025.
OxSpred, an eXtreme‐Gradient‐Boosting‐‐based supervised learning model, accurately annotates oxidative stress in innate immune cells at the single‐cell level, providing interpretable embeddings with significant biological relevance. This innovative tool revolutionizes the understanding of innate immune cell functions during inflammation and enhances ...
Po‐Yuan Chen, Tai‐Ming Ko
wiley   +1 more source

Metagenomic libraries data sets from the hypersaline benthic microbial mats of the Fraternidad Lagoon, Puerto Rico, using an indirect DNA extraction method. [PDF]

open access: yesMicrobiol Resour Announc
Nieves-Morales R   +5 more
europepmc   +1 more source

Predicting Performance of Hall Effect Ion Source Using Machine Learning

open access: yesAdvanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park   +8 more
wiley   +1 more source

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