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Identification of a small optimal subset of CpG sites as bio-markers from high-throughput DNA methylation profiles
- Source :
- BMC Bioinformatics, BMC Bioinformatics, Vol 9, Iss 1, p 457 (2008)
- Publication Year :
- 2008
- Publisher :
- Springer Science and Business Media LLC, 2008.
-
Abstract
- Background DNA 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. Results Using 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. Conclusion This 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.
- Subjects :
- Genetic Markers
Lung Neoplasms
Biology
lcsh:Computer applications to medicine. Medical informatics
Biochemistry
Artificial Intelligence
Structural Biology
medicine
Humans
Lung cancer
lcsh:QH301-705.5
Molecular Biology
Genetics
Applied Mathematics
Computational Biology
Methylation
DNA Methylation
medicine.disease
Computer Science Applications
Differentially methylated regions
lcsh:Biology (General)
CpG site
Genetic marker
DNA methylation
lcsh:R858-859.7
Illumina Methylation Assay
CpG Islands
DNA microarray
Research Article
Subjects
Details
- ISSN :
- 14712105
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- BMC Bioinformatics
- Accession number :
- edsair.doi.dedup.....4d20d179e3859b501e2a382de8e38544