Results 191 to 200 of about 1,787,003 (299)
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 +3 more
wiley +1 more source
The K-Step Spatial Sign Covariance Matrix [PDF]
The Sign Covariance Matrix is an orthogonal equivariant estimator of mul- tivariate scale. It is often used as an easy-to-compute and highly robust estimator.
Yadine, A., Croux, C., Dehon, C.
core
Advanced Intelligent Discovery, EarlyView.Deep learning‐based denoising models are applied to DNA data storage systems to enhance error reduction and data fidelity. By integrating DnCNN with DNA sequence encoding methods, the study demonstrates significant improvements in image quality and correction of substitution errors, revealing a promising path toward robust and efficient DNA‐based ...
Seongjun Seo +5 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.Sequential multicolor fluorescence imaging in dynamic microsystems is constrained by acquisition speed and excitation dose. This study introduces a real‐time framework to reconstruct spectrally separated channels from reduced cross‐channel acquisitions (frames containing mixed spectral contributions).
Juan J. Huaroto +3 more
wiley +1 more source
Evaluating covariance matrix forecasts in a value-at-risk framework [PDF]
Covariance matrix forecasts of financial asset returns are an important component of current practice in financial risk management. A wide variety of models, ranging from matrices of simple summary measures to covariance matrices implied from option ...
Christian A. Walter, Jose A. Lopez
core
Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics
Advanced Intelligent Discovery, EarlyView.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
Advanced Intelligent Discovery, EarlyView.AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia +2 more
wiley +1 more source
Different shades of default mode disturbance in schizophrenia: Subnodal covariance estimation in structure and function. [PDF]
Hum Brain Mapp, 2018 Lefort-Besnard J +11 more
europepmc +1 more source
A Critical Assessment of Bonding Descriptors for Predicting Materials Properties
Advanced Intelligent Discovery, EarlyView.The impact of new bonding descriptors in machine learning models for predicting material properties is assessed. Improvements are validated using significance tests, and new, intuitive descriptors for screening lattice thermal conductivity and projected force constants are introduced.
Aakash Ashok Naik +6 more
wiley +1 more source
Fast Component Pursuit for Large-Scale Inverse Covariance Estimation. [PDF]
KDD, 2016 Han L, Zhang Y, Zhang T.
europepmc +1 more source