Results 121 to 130 of about 2,131,360 (300)
ABSTRACT Objective The prognosis of glioblastoma (GBM) remains highly unfavorable, largely due to high tumor heterogeneity and an immunosuppressive microenvironment. However, the functional role of PANoptosis in this context is poorly understood. Methods Patients were stratified via K‐means clustering. A risk score model was constructed using prognosis‐
Langfei Tian +6 more
wiley +1 more source
Morphological Accuracy Data Clustering: A Novel Algorithm for Enhanced Cluster Analysis
In today’s data-driven world, we are constantly exposed to a vast amount of information. This information is stored in various information systems and is used for analysis and management purposes.
Abdel Fattah Azzam +4 more
doaj +1 more source
ABSTRACT Background Hereditary Spastic Paraplegia (HSP) comprises a group of rare genetic diseases characterized by length‐dependent axonal degeneration of the corticospinal tracts and dorsal columns, whose main clinical feature is spastic gait. Pathogenic variants in the SPG4 gene cause Spastic Paraplegia Type 4 (SPG4‐HSP), the most common form of HSP.
Gaia Fattorini +12 more
wiley +1 more source
نموذج متوازی هجین لخوارزمیات العنقدة للبیانات المستمرة بالاعتماد على التقسیم والکثافة للبیانات
Ammar Thaher Abd Alazeez
doaj +1 more source
ABSTRACT Objective Progression independent of relapse activity is a major determinant of long‐term disability in multiple sclerosis, but its immunopathologic basis remains incompletely understood. We investigated whether relapse‐independent progression in radiologically stable relapsing–remitting multiple sclerosis is associated with distinct ...
Antonio Bruno +19 more
wiley +1 more source
Data‐Driven SuStaIn Model of Disability Progression in Amyotrophic Lateral Sclerosis
ABSTRACT Objective To determine whether ordinal Subtype and Stage Inference (SuStaIn) applied to routine ALSFRS‐R item scores can identify reproducible disability progression patterns in amyotrophic lateral sclerosis (ALS) and provide clinically meaningful staging.
Giammarco Milella +5 more
wiley +1 more source
Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima
Simon Fong +3 more
doaj +1 more source
Objective Systemic lupus erythematosus (SLE) is a heterogenous inflammatory condition with widely varying global prevalence estimates. The frequency of SLE in the general population of Australia has been reported to be notably lower than contemporary estimates in countries such as the United States or United Kingdom, at 19 to 39 per 100,000 as opposed ...
Lucinda Roper +7 more
wiley +1 more source
Improved Selective Deep-Learning-Based Clustering Ensemble
Clustering ensemble integrates multiple base clustering results to improve the stability and robustness of the single clustering method. It consists of two principal steps: a generation step, which is about the creation of base clusterings, and a ...
Yue Qian +4 more
doaj +1 more source
Novel Algorithms For Clustering
Clustering is a method for discovering structure in data, widely used across many scientific disciplines. The two main clustering problems this dissertation considers are K-means and K-medoids. These are NP-hard problems in the number of samples and clusters, and both have well studied heuristic approximation algorithms. An example is Lloyd's algorithm
openaire +2 more sources

