ArticleBMC bioinformatics2008
Identification of a small optimal subset of CpG sites as bio-markers from high-throughput DNA methylation profiles.
Article in BMC bioinformatics, 2008. 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.
- Novel feature selection methods for construction of accurate epigenetic clocks.PLoS computational biology · 2022Article
- methCancer-gen: a DNA methylome dataset generator for user-specified cancer type based on conditional variational autoencoder.BMC bioinformatics · 2020Article
- Prosocial Emotion, Adolescence, and Warfare : DNA Methylation Associates with Culturally Salient Combat Variables.Human nature (Hawthorne, N.Y.) · 2019Article
- Correlation analyses of CpG island methylation of cluster of differentiation 4 protein with gene expression and T lymphocyte subpopulation traits.Asian-Australasian journal of animal sciences · 2018Article
- A beta-mixture model for dimensionality reduction, sample classification and analysis.BMC bioinformatics · 2011Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDNA methylation patterns have been shown to significantly correlate with different tissue types and disease states. High-throughput methylation arrays enable large-scale DNA methylation analysis to identify informative DNA methylation biomarkers. The identification of disease-specific methylation signatures is of fundamental and practical interest for risk assessment, diagnosis, and prognosis of diseases.
resultsUsing published high-throughput DNA methylation data, a two-stage feature selection method was developed to select a small optimal subset of DNA methylation features to precisely classify two sample groups. With this approach, a small number of CpG sites were highly sensitive and specific in distinguishing lung cancer tissue samples from normal lung tissue samples.
conclusionThis study shows that it is feasible to identify DNA methylation biomarkers from high-throughput DNA methylation profiles and that a small number of signature CpG sites can suffice to classify two groups of samples. The computational method we developed in the study is efficient to identify signature CpG sites from disease samples with complex methylation patterns.
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