Results 181 to 190 of about 123,395 (268)

Machine Learning‐Enhanced Random Matrix Theory Design for Human Immunodeficiency Virus Vaccine Development

Advanced Intelligent Discovery, EarlyView.
This study integrates random matrix theory (RMT) and principal component analysis (PCA) to improve the identification of correlated regions in HIV protein sequences for vaccine design. PCA validation enhances the reliability of RMT‐derived correlations, particularly in small‐sample, high‐dimensional datasets, enabling more accurate detection of ...
Mariyam Siddiqah, Muhammad Zulqarnain Zeb, Muhammad Husnain Zeb, Babar Shabbir   +3 more
wiley   +1 more source

Automating AI Discovery for Biomedicine Through Knowledge Graphs and Large Language Models Agents

Advanced Intelligent Discovery, EarlyView.
This work proposes a novel framework that automates biomedical discovery by integrating knowledge graphs with multiagent large language models. A biologically aligned graph exploration strategy identifies hidden pathways between biomedical entities, and specialized agents use this pathway to iteratively design AI predictors and wet‐lab validation ...
Naafey Aamer, Muhammad Nabeel Asim, Shan Munir, Andreas Dengel   +3 more
wiley   +1 more source

Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation. [PDF]

Measurement (Lond)
Jeon ES, Lee J, Lim H, Save OM, Lee H, Turaga P.   +5 more
europepmc   +1 more source

Automated Alignment Powered by Computer Vision Streamlines the Two‐Photon Polymerization‐Based Micro 3D Printing of Multiscale and Multimaterial Structures

Advanced Intelligent Discovery, EarlyView.
Two‐photon polymerization enables high‐resolution microfabrication, but performing alignment when printing multiple structures is difficult. Here, we present a fast, robust, and open‐source protocol for automated alignment on Nanoscribe systems. Achieving ≈0.4 μm accuracy in under 5 s, our protocol reduces time and error in multimaterial printing. This
Daniel Maher, Valentina Mendoza Martinez, Marcin Piotr Piekarczyk, Stefan Sützl, Ada‐Ioana Bunea   +4 more
wiley   +1 more source

Morphology-aware distillation for lightweight retinal vessel segmentation across fundus photography and OCT angiography. [PDF]

Front Cell Dev Biol
Shu H, Yuan Q, Pan Y, Kong W, Feng Y, Peng C, Li K, Nguyen LM, Racharak T, Xin J, Sun G.   +10 more
europepmc   +1 more source

LLM‐Based Scientific Assistants for Knowledge Extraction: Which Design Choices Matter?

Advanced Intelligent Discovery, EarlyView.
A comprehensive framework for optimizing Large Language Models in domain‐specific applications is introduced. The LLM Playground integrates Prompt Engineering, knowledge augmentation, and advanced reasoning strategies to enable systematic comparison of architectures and base models.
David Exler, Manuel Raimann, Marc Münker, Maxim Rosin, Joaquin E. Urrutia Gómez, Christof M. Niemeyer, Markus Reischl, Luca Rettenberger   +7 more
wiley   +1 more source

Cross-Architecture Knowledge Distillation for Histopathological Image Analysis. [PDF]

IEEE Access
Boudissa S, Kawanaka H, Aronow B, Prasath VBS.   +3 more
europepmc   +1 more source

AI Powered Biobanks From Static Archives to Dynamic Discovery Engines

Advanced 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, Yutian Feng, Wenrui Ma, Yuxin Su, Li Min, Juan Liu   +5 more
wiley   +1 more source

"A Change in Culture From It Being an Optional Extra to an Integral Part of Education"-Teacher's Experiences of Teaching Relationships and Sexuality Education (RSE) to Learners With Severe/Profound Intellectual Disabilities in the Republic of Ireland. [PDF]

J Appl Res Intellect Disabil
Connolly L, O'Mahony K, Scully C.
europepmc   +1 more source

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source