Results 71 to 80 of about 50,718 (252)

Functional principal component analysis and sparse-group LASSO to identify associations between biomarker trajectories and mortality among hospitalized SARS-CoV-2 infected individuals

BMC Medical Research Methodology, 2023
Background A substantial body of clinical research involving individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evaluated the association between in-hospital biomarkers and severe SARS-CoV-2 outcomes, including ...
Tingyi Cao, Harrison T. Reeder, Andrea S. Foulkes   +2 more
doaj   +1 more source

Understanding and Mitigating the Risk of Accelerated Biological Aging in Survivors of Cancer: A Scoping Review

Aging and Cancer, EarlyView.
Cancer treatment is associated with measurable acceleration of biological aging across epigenetic, telomere, senescence, and immune biomarkers. However, biomarker validation and interventional strategies remain limited, especially in hematologic malignancies, underscoring the need for standardized multi‐omic aging assessments and adequately powered ...
Moataz Ellithi, Emily Herdman, Danielle Westmark, Vijaya R. Bhatt   +3 more
wiley   +1 more source

Early Clinical and Cerebrospinal Fluid Predictors of 1‐Year Recurrence in Autoimmune GFAP Astrocytopathy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Autoimmune glial fibrillary acidic protein astrocytopathy (GFAP‐A) is an inflammatory central nervous system disorder with variable outcomes. Relapse occurs in a subset of patients, but early predictors remain unclear. We aimed to identify admission‐available features associated with 1‐year recurrence and develop an interpretable ...
Qingting Hong, Yuping Xiao, Yuhan Wu, Ziyuan Huang, Weishan Liang, Li Xiao, Yaqing Shu, Tingting Lu, Yanyu Chang, Wei Qiu, Yuge Wang   +10 more
wiley   +1 more source

Image restoration based on adaptive switching between synthesis and analysis sparse regularisation

The Journal of Engineering, 2019
Both synthesis and analysis sparse regularisation have been successfully applied to solve various inverse vision problems. The authors find an optimisation model to combine the power of the dual sparse prior models for image restoration.
Huahua Chen, Jiling Xue, Yu Lu, Chunsheng Guo   +3 more
doaj   +1 more source

Clustering Algorithm for High-Dimensional Data Under New Dimensionality Reduc-tion Criteria

Jisuanji kexue yu tansuo, 2020
In order to solve the problem that principal component analysis (PCA) algorithm can??t deal with the reduction of clustering accuracy after high dimensional data reduction, a new attribute space concept is proposed.
WAN Jing, WU Fan, HE Yunbin, LI Song
doaj   +1 more source

Diffusion Basis Spectrum Imaging of Tissue Microstructure and Early Treatment in Multiple Sclerosis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Conventional MRI cannot resolve the coexisting pathological processes within multiple sclerosis tissue. Diffusion Basis Spectrum Imaging decomposes the diffusion signal into fiber, cell, and hindered ratios indexing axonal integrity, cellularity, and extracellular space, respectively.
Apisit Kaewsanit, Aime Luna, Bradley Judge, Nicole Shelley, Karl Friedrichsen, Jayashree Rajamanickam, Zhude Tu, Yong Wang, Tammie L. S. Benzinger, Qing Wang, Matthew R. Brier   +10 more
wiley   +1 more source

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga, Marco Zaffalon   +3 more
wiley   +1 more source

Full regularization path for sparse principal component analysis [PDF]

Proceedings of the 24th international conference on Machine learning, 2007
Given a sample covariance matrix, we examine the problem of maximizing the variance explained by a particular linear combination of the input variables while constraining the number of nonzero coefficients in this combination. This is known as sparse principal component analysis and has a wide array of applications in machine learning and engineering ...
Alexandre d'Aspremont, Francis R. Bach, Laurent El Ghaoui   +2 more
openaire   +2 more sources

Risk factor analysis for stunting incidence using sparse categorical principal component logistic regression

MethodsX
The risk factors for stunting incidence involve categorical data in both the response and predictor variables. Therefore, we developed a sparse categorical principal component logistic regression model capable of handling data with multicollinearity. The
Anna Islamiyati, Muhammad Nur, Abdul Salam, Wan Zuki Azman Wan Muhamad, Dwi Auliyah   +4 more
doaj   +1 more source

Discrepancies Between Micro Versus Macroscale Viscoelastic Properties in a Microplastic‐Tissue Composite Model

Advanced Engineering Materials, EarlyView.
This study explores the discrepancies between bulk mechanics and spatial mapping in a tissue‐mimetic hydrogel model. Microplastic particles create localized stiff mechanical microenvironments that are detectable by cellular‐scale nanoindentation but leave bulk properties unchanged as measured by rheology.
Ahron T. Verschleisser, Baptiste Le Roi, Omri Leshem, Joshua M. Grolman   +3 more
wiley   +1 more source