Results 181 to 190 of about 285,691 (282)
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
Large-Scale Simultaneous Testing of Cross-Covariance Matrices with Applications to PheWAS. [PDF]
Stat Sin, 2019 Cai T, Cai TT, Liao K, Liu W.
europepmc +1 more source
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
Development and Evaluation of Geostatistical Methods for Non-Euclidean-Based Spatial Covariance Matrices. [PDF]
Math Geosci, 2019 Davis BJK, Curriero FC.
europepmc +1 more source
Advanced Intelligent Discovery, EarlyView.A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.We propose a residual‐based adversarial‐gradient moving sample (RAMS) method for scientific machine learning that treats samples as trainable variables and updates them to maximize the physics residual, thereby effectively concentrating samples in inadequately learned regions.
Weihang Ouyang +4 more
wiley +1 more source
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
Video based object representation and classification using multiple covariance matrices. [PDF]
PLoS One, 2017 Zhang Y, Liu Q.
europepmc +1 more source
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.A laser pointer‐guided robotic grasping method for arbitrary objects based on promptable segment anything model and force‐closure analysis is presented. Grasp generation methods based on force‐closure analysis can calculate the optimal grasps for objects through their appearances. However, the limited visual perception ability makes robots difficult to
Yan Liu +5 more
wiley +1 more source
TESTING HIGH-DIMENSIONAL COVARIANCE MATRICES, WITH APPLICATION TO DETECTING SCHIZOPHRENIA RISK GENES. [PDF]
Ann Appl Stat, 2017 Zhu L, Lei J, Devlin B, Roeder K.
europepmc +1 more source