Results 221 to 230 of about 1,343,714 (312)
Obstructive Sleep Apnea and the Risk of Age-Related Macular Degeneration. [PDF]
Shadmani A, Kovach JL.
europepmc +1 more source
AI Powered Biobanks From Static Archives to Dynamic Discovery Engines
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
Dietary intake, lifestyle behaviors, and sleep architecture among night-shift medical residents: a cross-sectional study. [PDF]
Rousan MY +3 more
europepmc +1 more source
This article implements a unified human digital twin framework that integrates cutting edge actuation, sensing, simulation, and bidirectional feedback capability. The approach includes integrating multimodal sensing, AI, and biomechanical simulation into one compact system.
Tajbeed Ahmed Chowdhury +4 more
wiley +1 more source
GIGEM (Group Isolation Gauge Effect Metrics), a Software Suite for Analyzing Social Isolation-induced Sleep Loss and Multi-batch Experiments in <i>Drosophila</i>. [PDF]
Doria EI, Hammood F, Yan J, Li W.
europepmc +1 more source
Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong +5 more
wiley +1 more source
Sleep Disturbance, Treatment-Related Adverse Events, and Psychological Distress in Breast Cancer Patients: A Prospective Cohort Study. [PDF]
Zhao F +11 more
europepmc +1 more source
scTIGER2.0 is a deep‐learning framework that infers gene regulatory networks from single‐cell RNA sequencing data. By integrating correlation, pseudotime ordering, deep learning and bootstrap‐based significance testing, it reduces false positives and reveals directional gene interactions.
Nishi Gupta +3 more
wiley +1 more source

