ArticleBMC bioinformatics2018
diceR: an R package for class discovery using an ensemble driven approach.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Evaluation of Supervised Machine Learning Algorithms for Cell Phenotype Classification Using Single-Cell RNA Sequencing Data.Frontiers in genetics · 2022Pooled it
- A randomized clinical trial to evaluate feasibility, tolerability, and preliminary target engagement for a novel executive working memory training in adolescents with ADHD.Behaviour research and therapy · 2024Trial
- Ensemble clustering: A practical tutorial.Behavior research methods · 2026Article
- Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering.Nature methods · 2026Article
- Coping with an Uncertain or Poor Cancer Prognosis as an Adolescent or Young Adult: A Cross-Sectional Cluster Analysis.Current oncology (Toronto, Ont.) · 2026Article
- The neuronal chromatin landscape in brains from individuals with schizophrenia is linked to early fetal development.Nature neuroscience · 2025Article
- The ICN-UN Battery: A Machine Learning-Optimized Tool for Expeditious Alzheimer's Disease Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering.bioRxiv : the preprint server for biology · 2025Article
- Transposable element dynamics in glioblastoma stem cells: insights from locus-specific quantification.Mobile DNA · 2025Article
- The Diagnostic Value of Visual Evoked Potentials in Chronic Disorders of Consciousness.Annals of clinical and translational neurology · 2025Article
- Patterns in GP appointment systems: a cluster analysis of 3480 English practices.The British journal of general practice : the journal of the Royal College of General Practitioners · 2025Article
- Trophic convergence of marine vertebrate communities worldwide.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Exploring functional connectivity in clinical and data-driven groups of preterm and term adults.Brain communications · 2025Article
- Article
- The genomic landscape of 2,023 colorectal cancers.Nature · 2024Article
- Poly-GR repeats associated with ALS/FTD geneScience signaling · 2024Article
- Consensus clustering with missing labels (ccml): a consensus clustering tool for multi-omics integrative prediction in cohorts with unequal sample coverage.Briefings in bioinformatics · 2023Article
- The Prognostic Effect of Immune Cell Infiltration Depends on Molecular Subtype in Endometrioid Ovarian Carcinomas.Clinical cancer research : an official journal of the American Association for Cancer Research · 2023Article
- Parsing brain-behavior heterogeneity in very preterm born children using integrated similarity networks.Translational psychiatry · 2023Article
- clusterBMA: Bayesian model averaging for clustering.PloS one · 2023Article
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Authors and funding
2 authors.
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Abstract
backgroundGiven a set of features, researchers are often interested in partitioning objects into homogeneous clusters. In health research, cancer research in particular, high-throughput data is collected with the aim of segmenting patients into sub-populations to aid in disease diagnosis, prognosis or response to therapy. Cluster analysis, a class of unsupervised learning techniques, is often used for class discovery. Cluster analysis suffers from some limitations, including the need to select up-front the algorithm to be used as well as the number of clusters to generate, in addition, there may exist several groupings consistent with the data, making it very difficult to validate a final solution. Ensemble clustering is a technique used to mitigate these limitations and facilitate the generalization and reproducibility of findings in new cohorts of patients.
resultsWe introduce diceR (diverse cluster ensemble in R), a software package available on CRAN: https://CRAN.R-project.org/package=diceR
conclusionsdiceR is designed to provide a set of tools to guide researchers through a general cluster analysis process that relies on minimizing subjective decision-making. Although developed in a biological context, the tools in diceR are data-agnostic and thus can be applied in different contexts.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.