1816 papers
Computational simulations reveal that while molecular crowding minimally affects the average water transport rate through aquaporins, it significantly increases transport variability and membrane instability, suggesting that variance serves as a more sensitive indicator of crowding-induced dynamics than mean rates.
ExposOmix-Fed is a federated, site-invariant protocol that integrates environmental exposome, multi-omics, and abdominal MRI data for colorectal cancer risk stratification within a privacy-preserving framework, demonstrating via in-silico validation on calibrated synthetic data that it effectively recovers injected cross-modal signals and selection-bias-corrected hazard ratios while enabling auditable exposure–molecule interaction maps.
This study utilizes an integrated multi-omics analysis of TCGA-LUAD data combined with machine learning and network analysis to identify and validate five prognostic biomarkers (BIRC3, PSMB1, PSMA4, PSMC4, and TNFRSF12A) that are significantly associated with poor overall survival in lung adenocarcinoma, despite showing limited diagnostic utility.
This study demonstrates that neglecting arterial wall compliance in cardiovascular simulations introduces frequency-dependent errors that progressively increase with heart rate, as rigid-wall models fail to capture the compliance-driven storage and damping mechanisms essential for accurate hemodynamic predictions.
This paper, which originally proposed that exogenous HMGB1 promotes pancreatic cancer progression by facilitating crosstalk between tumor cells and myeloid-derived suppressor cells, has been withdrawn by the authors for corrections and should not be cited.
This paper introduces a scalable geometric framework that utilizes the Off–log metric for correlation matrices and Grassmannian subspace discrimination for eigenvector analysis to enhance the sensitivity and predictive performance of fMRI-based brain network modeling while maintaining compatibility with standard machine learning workflows.
This paper presents a novel AI-driven, physics-based lumped-element model that integrates deep learning-extracted vocal fold dynamics with a spring-damper-mass representation of the respiratory system to simulate voice production and predict non-invasively measurable parameters like subglottal pressure and energy transfer mechanisms.
The paper introduces PltScanner, a lightweight ensemble classifier that accurately identifies and corrects misannotations of platelets in scRNA-seq data while revealing their functional heterogeneity and interactions within complex tissue microenvironments.
The paper introduces CryoDCOV, a deep-learning framework that utilizes decoupled Gaussian mixture models to effectively separate and reconstruct conformational and compositional heterogeneity in cryo-EM data, thereby enabling the generation of high-resolution, interpretable 3D structures of dynamic macromolecular ensembles.
This study demonstrates the successful application of a 63K SNP array to genotype 49 caribou in Canadian zoological facilities, providing the first molecular characterization of this managed population to optimize breeding strategies, preserve genetic diversity, and support future wild reintroduction efforts.