AIMC Topic: Cluster Analysis

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PND.heter.cluster: An R package for estimating cluster-specific treatment effects in partially nested designs.

Behavior research methods
Partially nested designs-where clustering occurs in some but not all study arms-are common in psychological and behavioral research. In these designs, clustering often arises in the treatment arm due to the treatment delivery, such as individuals clu...

Identification of clinically meaningful, overlapping obstructive respiratory disease subtypes via data-driven approaches in a primary care population.

BMC pulmonary medicine
BACKGROUND: Obstructive respiratory conditions, including asthma, bronchiectasis, and chronic obstructive pulmonary disease (COPD), are increasingly recognised as heterogeneous syndromes with significant overlap. Multiple disease pathways contribute ...

Smart defense based on explainable stacked machine learning architecture for securing internet of health things with K-means clustering.

Scientific reports
The Internet of Health Things (IoHT) transformed current healthcare by facilitating real-time patient monitoring and remote diagnosis via networked medical equipment. The advanced prevalence of interconnected medical devices creates substantial vulne...

Identifying and predicting dietary patterns in the Dutch population using machine learning.

European journal of nutrition
PURPOSE: Nutritional epidemiological research is shifting its focus from individual nutrients to dietary patterns, which challenges traditional statistical methods. Here, we aim to apply various machine learning algorithms to identify and predict die...

Enhancing disease clustering through symptom-based analysis and large language model interpretations.

Scientific reports
Humans face various diseases that are mainly caused by environmental conditions and living habits. These diseases exhibit several symptoms and can share a relationship based on their symptoms. The identification and interpretation of these groups of ...

Spatial domain identification method based on multi-view graph convolutional network and contrastive learning.

PLoS computational biology
Spatial transcriptomics is a rapidly developing field of single-cell genomics that quantitatively measures gene expression while providing spatial information within tissues. A key challenge in spatial transcriptomics is identifying spatially structu...

Insight into the influence of various cultivation regions on the identification of metabolites from Capsella bursa-pastoris via a clustering algorithm.

Scientific reports
This study focused on the regional differentiation of metabolites of C. bursa-pastoris cultivated across Korea via UHPLC‒HRMS‒based untargeted metabolomics. Extensive screening was conducted on samples collected from five distinct sites in 20 cities,...

Combining multifaceted aspects of technology innovations through fuzzy clustering of multilayer networks.

PloS one
This study advances a novel multilayer network model to explore the connection between different aspects of Technological Innovation in European Union (EU) countries. We follow a fuzzy clustering approach and consider three variables: Research and De...

More Sophisticated Is Not Always Better: A Comparison of Similarity Measures for Unsupervised Learning of Pathways in Biomolecular Simulations.

The journal of physical chemistry. B
Finding process pathways in molecular simulations such as the unbinding paths of small molecule ligands from their binding sites at protein targets in a set of trajectories via unsupervised learning approaches requires the definition of a suitable si...

Clustering and Analyzing Ensembles of Residue Interaction Networks from Molecular Dynamics Simulations.

Journal of chemical information and modeling
Network methods and molecular dynamics (MD) simulations have become essential tools for studying protein dynamics. However, applying network methods to MD simulations of flexible proteins is a major challenge, since the high conformational heterogene...