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Cluster Analysis

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Learning clustering-friendly representations via partial information discrimination and cross-level interaction.

Neural networks : the official journal of the International Neural Network Society
Despite significant advances in the deep clustering research, there remain three critical limitations to most of the existing approaches. First, they often derive the clustering result by associating some distribution-based loss to specific network l...

Text summarization for pharmaceutical sciences using hierarchical clustering with a weighted evaluation methodology.

Scientific reports
In the pharmaceutical industry, there is an abundance of regulatory documents used to understand the current regulatory landscape and proactively make project decisions. Due to the size of these documents, it is helpful for project teams to have info...

Backdoor attacks on unsupervised graph representation learning.

Neural networks : the official journal of the International Neural Network Society
Unsupervised graph learning techniques have garnered increasing interest among researchers. These methods employ the technique of maximizing mutual information to generate representations of nodes and graphs. We show that these methods are susceptibl...

Gauging road safety advances using a hybrid EWM-PROMETHEE II-DBSCAN model with machine learning.

Frontiers in public health
INTRODUCTION: Enhancing road safety conditions alleviates socioeconomic hazards from traffic accidents and promotes public health. Monitoring progress and recalibrating measures are indispensable in this effort. A systematic and scientific decision-m...

Machine learning identifies clusters of the normal adolescent spine based on sagittal balance.

Spine deformity
PURPOSE: This study applied a machine learning semi-supervised clustering approach to radiographs of adolescent sagittal spines from a single pediatric institution to identify patterns of sagittal alignment in the normal adolescent spine. We sought t...

Identification of copper death-associated molecular clusters and immunological profiles for lumbar disc herniation based on the machine learning.

Scientific reports
Lumbar disc herniation (LDH) is a common clinical spinal disorder, yet its etiology remains unclear. We aimed to explore the role of cuproptosis-related genes (CRGs) and identify potential diagnostic biomarkers. Our analysis involved interrogating th...

Definition and Validation of Prognostic Phenotypes in Moderate Aortic Stenosis.

JACC. Cardiovascular imaging
BACKGROUND: Adverse outcomes from moderate aortic stenosis (AS) may be caused by progression to severe AS or by the effects of comorbidities. In the absence of randomized trial evidence favoring aortic valve replacement (AVR) in patients with moderat...

Efficient model-informed co-segmentation of tumors on PET/CT driven by clustering and classification information.

Computers in biology and medicine
Automatic tumor segmentation via positron emission tomography (PET) and computed tomography (CT) images plays a critical role in the prevention, diagnosis, and treatment of this disease via radiation oncology. However, segmenting these tumors is chal...

Unraveling phenotypic heterogeneity in stanford type B aortic dissection patients through machine learning clustering analysis of cardiovascular CT imaging.

Hellenic journal of cardiology : HJC = Hellenike kardiologike epitheorese
OBJECTIVE: Aortic dissection remains a life-threatening condition necessitating accurate diagnosis and timely intervention. This study aimed to investigate phenotypic heterogeneity in patients with Stanford type B aortic dissection (TBAD) through mac...

Accurate neuron segmentation method for one-photon calcium imaging videos combining convolutional neural networks and clustering.

Communications biology
One-photon fluorescent calcium imaging helps understand brain functions by recording large-scale neural activities in freely moving animals. Automatic, fast, and accurate active neuron segmentation algorithms are essential to extract and interpret in...