AIMC Topic: Cluster Analysis

Clear Filters Showing 1241 to 1250 of 1443 articles

Toward a Universal Map of EEG: A Semantic, Low-Dimensional Manifold for EEG Classification, Clustering, and Prognostication.

Annals of neurology
OBJECTIVE: Prognostication in patients with disorders of consciousness (DOCs) remains challenging because of heterogeneous etiologies, pathophysiologies and, consequently, highly variable electroencephalograms (EEGs). Here, we use EEG patterns that a...

ADEPT: An advanced data exploration and processing tool for clinical data insights.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The rapid growth of clinical data creates challenges in analysis and interpretation for medical professionals. To address these issues, we developed the Advanced Data Exploration and Processing Tool (ADEPT), integrating data...

Patterns in GP appointment systems: a cluster analysis of 3480 English practices.

The British journal of general practice : the journal of the Royal College of General Practitioners
BACKGROUND: In response to increasing demand for appointments, UK general practices have adopted a range of appointment systems. These systems vary widely in implementation. These changes have not yet been clearly described.

Phenotypic clustering analysis of patients rejected for mitral valve interventions: implications for future transcatheter technologies.

European heart journal. Cardiovascular Imaging
AIMS: Although several treatment options are available for patients with severe mitral regurgitation (MR), a significant proportion of patients remain ineligible for any mitral valve (MV) intervention. We aimed to analyse the phenotypic characteristi...

Iterative clustering algorithm G-DESC-E and pan-cancer key gene analysis based on single-cell sequencing data.

Briefings in bioinformatics
Single-cell sequencing technology has profoundly revolutionized the field of cancer genomics, enabling researchers to explore gene expression profiles at the resolution of individual cells. Despite its extensive applications in the study of cancer ge...

Phenotype-driven risk stratification of cerebral aneurysms using Shapley Additive Explanations-based supervised clustering: a novel approach to rupture prediction.

Neurosurgical focus
OBJECTIVE: The aim of this study was to address the limitations of traditional aneurysm risk scoring systems and computational fluid dynamics (CFD) analyses by applying a supervised clustering framework to identify distinct aneurysm phenotypes and im...

Differentiable graph clustering with structural grouping for single-cell RNA-seq data.

Bioinformatics (Oxford, England)
MOTIVATION: Clustering cells into subpopulations is one of the most crucial tasks in single-cell RNA sequencing (scRNA-seq) data analysis, which provides support for biological research at cellular level. With the development of graph neural networks...

A novel Harris Hawks Optimization-based clustering method for elucidating genetic associations in osteoarthritis and Diverse Cancer Types.

Computers in biology and medicine
Considering the high incidence of osteoarthritis (OA), especially of the knee and hip, this study explores the possible genetic associations between OA and cancer types, including cancers of the bladder, kidney, breast, and prostate. The objective of...

A cluster attention-based multiple instance learning network for enhancing histopathological image interpretation.

Computers in biology and medicine
BACKGROUND: Histopathological diagnosis involves examining abnormal architectural patterns and cellular-level changes. Whole slide images (WSIs) provide comprehensive digital representations of tissue samples, enabling detailed analysis and interpret...

A novel self-supervised graph clustering method with reliable semi-supervision.

Neural networks : the official journal of the International Neural Network Society
Cluster analysis, as a core technique in unsupervised learning, has widespread applications. With the increasing complexity of data, deep clustering, which integrates the advantages of deep learning and traditional clustering algorithms, demonstrates...