Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

ClustAll: An R package for patient stratification in complex diseases.

PLoS computational biology - 1 Dec 2024

Ortega-Legarreta Asier, Palomino-Echeverria Sara, Huergo Estefania, Lagani Vincenzo, Kiani Narsis A, Rautou Pierre-Emmanuel, Picola Nuria Planell, Tegner Jesper, Gomez-Cabrero David

Abstract excerpt

In the era of precision medicine, it is necessary to understand heterogeneity among patients with complex diseases to improve personalized prevention and management strategies. Here, we introduce ClustAll, a Bioconductor package designed for unsupervised patient stratification using clinical data. ClustAll is based on the previously validated methodology ClustAll, a clustering framework that effectively handles...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Cluster Analysis

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Computational Biology

Share to Phenotype

Share to Precision Medicine

Share to Software

Source

Open in PubMed Open full text

Identifiers

PMID: 39671459
DOI: 10.1371/journal.pcbi.1012656
PMCID: PMC11676920

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

ClustAll: An R package for patient stratification in complex diseases.PMID 39671459Read ↗

Structure-informed clustering for population stratification in association studies.Semantic proximity Read ↗

TPClust: Temporal Profile-Guided Subtyping Using High-Dimensional Omics DataSemantic proximity Read ↗

Qluster: An Easy-to-Implement Generic Workflow for Robust Clustering of Health DataSemantic proximity Read ↗

A Clustered Multiclass Likelihood-Ratio Ensemble Method for Family-Based Association Analysis Accounting for Phenotypic Heterogeneity.Semantic proximity Read ↗

cohorts: A Python package for clinical ‘omics data managementSemantic proximity Read ↗

CluStrat: a structure informed clustering strategy for population stratificationSemantic proximity Read ↗

Omada: Robust clustering of transcriptomes through multiple testingSemantic proximity Read ↗

An unsupervised machine learning method for discovering patient clusters based on genetic signatures.Semantic proximity Read ↗

SUBSTRA: Supervised Bayesian Patient StratificationSemantic proximity Read ↗

Automatic clustering of self-defined groups to minimize false disease associations and maximize within-group genetic similaritySemantic proximity Read ↗

Using Latent Class Analysis to Identify Hidden Clinical Phenotypes.Semantic proximity Read ↗

Multi-view singular value decomposition for disease subtyping and genetic associations.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.