ArticleBMC bioinformatics2011
A novel approach to the clustering of microarray data via nonparametric density estimation.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Ten quick tips for biomarker discovery and validation analyses using machine learning.PLoS computational biology · 2022Article
- Immune translational control by CPEB4 regulates intestinal inflammation resolution and colorectal cancer development.iScience · 2022Article
- Skin injury model classification based on shape vector analysis.BMC medical imaging · 2012Article
- Whole miRNome-wide differential co-expression of microRNAs.Genomics, proteomics & bioinformatics · 2012Article
- A hybrid feature subset selection algorithm for analysis of high correlation proteomic data.Journal of medical signals and sensors · 2012Article
- β-empirical Bayes inference and model diagnosis of microarray data.BMC bioinformatics · 2012Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCluster analysis is a crucial tool in several biological and medical studies dealing with microarray data. Such studies pose challenging statistical problems due to dimensionality issues, since the number of variables can be much higher than the number of observations.
resultsHere, we present a general framework to deal with the clustering of microarray data, based on a three-step procedure: (i) gene filtering; (ii) dimensionality reduction; (iii) clustering of observations in the reduced space. Via a nonparametric model-based clustering approach we obtain promising results both in simulated and real data.
conclusionsThe proposed algorithm is a simple and effective tool for the clustering of microarray data, in an unsupervised setting.
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Registered trials
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