AIMC Topic: Cluster Analysis

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Unveiling sub-populations in critical care settings: a real-world data approach in COVID-19.

Frontiers in public health
BACKGROUND: Disease presentation and progression can vary greatly in heterogeneous diseases, such as COVID-19, with variability in patient outcomes, even within the hospital setting. This variability underscores the need for tailored treatment approa...

Bayesian unsupervised clustering identifies clinically relevant osteosarcoma subtypes.

Briefings in bioinformatics
Identification of cancer subtypes is a critical step for developing precision medicine. Most cancer subtyping is based on the analysis of RNA sequencing (RNA-seq) data from patient cohorts using unsupervised machine learning methods such as hierarchi...

m6A-related genes and their role in Parkinson's disease: Insights from machine learning and consensus clustering.

Medicine
Parkinson disease (PD) is a chronic neurological disorder primarily characterized by a deficiency of dopamine in the brain. In recent years, numerous studies have highlighted the substantial influence of RNA N6-methyladenosine (m6A) regulators on var...

Clustering Electrophysiological Predisposition to Binge Drinking: An Unsupervised Machine Learning Analysis.

Brain and behavior
BACKGROUND: The demand for fresh strategies to analyze intricate multidimensional data in neuroscience is increasingly evident. One of the most complex events during our neurodevelopment is adolescence, where our nervous system suffers constant chang...

Machine learning-based clustering identifies obesity subgroups with differential multi-omics profiles and metabolic patterns.

Obesity (Silver Spring, Md.)
OBJECTIVE: Individuals living with obesity are differentially susceptible to cardiometabolic diseases. We hypothesized that an integrative multi-omics approach might improve identification of subgroups of individuals with obesity who have distinct ca...

Precision DNA methylation typing via hierarchical clustering of Nanopore current signals and attention-based neural network.

Briefings in bioinformatics
Decoding DNA methylation sites through nanopore sequencing has emerged as a cutting-edge technology in the field of DNA methylation research, as it enables direct sequencing of native DNA molecules without the need for prior enzymatic or chemical tre...

HCS-hierarchical algorithm for simulation of omics datasets.

Bioinformatics (Oxford, England)
MOTIVATION: Analysis of the omics data with the help of machine learning (ML) methods is limited by small sample sizes and a large number of variables. One possible approach to deal with such data is using algorithms for feature selection and reducin...

CoRTEx: contrastive learning for representing terms via explanations with applications on constructing biomedical knowledge graphs.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Biomedical Knowledge Graphs play a pivotal role in various biomedical research domains. Concurrently, term clustering emerges as a crucial step in constructing these knowledge graphs, aiming to identify synonymous terms. Due to a lack of ...

Innovative Approaches to Gender Classification through Unsupervised Machine Learning and Multi-Activity Fusion.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In the last decade, gender recognition has garnered significant attention for its diverse applications in healthcare, sports, rehabilitation, and wearable electronics. This study offers a wearable sensor device to record various activities using iner...

Dynamic multi-hypergraph structure learning for disease diagnosis on multimodal data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
With its superior capability in complex data modeling, hypergraph computation is a powerful tool for many applications. In this work, we propose using hypergraph computation for disease prediction. Hypergraphs allow for the representation of higher-o...