AIMC Topic: Cluster Analysis

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Analyzing and predicting global happiness index via integrated multilayer clustering and machine learning models.

PloS one
This study addresses the research objective of predicting global happiness and identifying its key drivers. We propose a novel predictive framework that integrates unsupervised and supervised machine learning techniques to uncover the complex pattern...

Interpretable unsupervised neural network structure for data clustering via differentiable reconstruction of ONMF and sparse autoencoder.

Neural networks : the official journal of the International Neural Network Society
Neural networks, while powerful, often face significant challenges in terms of interpretability, particularly in clustering tasks. Traditional methods typically rely on post-hoc explanations or supervised learning, which limit their ability to provid...

GINClus: RNA structural motif clustering using graph isomorphism network.

NAR genomics and bioinformatics
Ribonucleic acid (RNA) structural motif identification is a crucial step for understanding RNA structure and functionality. Due to the complexity and variations of RNA 3D structures, identifying RNA structural motifs is challenging and time-consuming...

LACE-UP: An ensemble machine-learning method for health subtype classification on multidimensional binary data.

Proceedings of the National Academy of Sciences of the United States of America
Disease and behavior subtype identification is of significant interest in biomedical research. However, in many settings, subtype discovery is limited by a lack of robust statistical clustering methods appropriate for binary data. Here, we introduce ...

BL-FlowSOM: Consistent and Highly Accelerated FlowSOM Based on Parallelized Batch Learning.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
The recent increase in the dimensionality of cytometry data has led to the development of various computational analysis methods. FlowSOM is one of the best-performing clustering methods but has room for improvement in terms of the consistency and sp...

Exploratory cluster analysis of IL2Ra and associated biomarkers and complications after blunt chest trauma.

The journal of trauma and acute care surgery
BACKGROUND: Rib fractures compromise approximately 40% of all fractures in the United States. Despite their prevalence, the relationship between rib fractures, solid organ injuries, and immune responses remains poorly understood. This exploratory stu...

Children on wheels: Identifying crash determinants using cluster correspondence analysis.

Accident; analysis and prevention
Child bicyclists (14 years old and younger) are among the most vulnerable road users, facing significant risks of crashes that often result in severe injuries or fatalities. This study aims to identify key factors influencing child bicyclist crashes ...

scAMZI: attention-based deep autoencoder with zero-inflated layer for clustering scRNA-seq data.

BMC genomics
BACKGROUND: Clustering scRNA-seq data plays a vital role in scRNA-seq data analysis and downstream analyses. Many computational methods have been proposed and achieved remarkable results. However, there are several limitations of these methods. First...