Cancer Biomarkers from Genome-Scale DNA Methylation: Comparison of Evolutionary and Semantic Analysis Methods
| Autor: | Ioannis Valavanis, Panagiotis Georgiadis, Eleftherios Pilalis, Aristotelis Chatziioannou, Soterios A. Kyrtopoulos |
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| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Biomedical Engineering
Bioengineering Feature selection Computational biology Biology Biochemistry Article lcsh:Biochemistry gene ontology tree breast cancer medicine lcsh:QD415-436 KEGG Selection (genetic algorithm) Genetics DNA methylation evolutionary algorithm B-cell lymphoma Cancer Methylation epigenetic biomarker medicine.disease CpG site classification Cancer biomarkers graph-theory Biotechnology |
| Zdroj: | Microarrays Volume 4 Issue 4 Pages 647-670 Microarrays, Vol 4, Iss 4, Pp 647-670 (2015) |
| ISSN: | 2076-3905 |
| DOI: | 10.3390/microarrays4040647 |
| Popis: | DNA methylation profiling exploits microarray technologies, thus yielding a wealth of high-volume data. Here, an intelligent framework is applied, encompassing epidemiological genome-scale DNA methylation data produced from the Illumina's Infinium Human Methylation 450K Bead Chip platform, in an effort to correlate interesting methylation patterns with cancer predisposition and, in particular, breast cancer and B-cell lymphoma. Feature selection and classification are employed in order to select, from an initial set of ~480,000 methylation measurements at CpG sites, predictive cancer epigenetic biomarkers and assess their classification power for discriminating healthy versus cancer related classes. Feature selection exploits evolutionary algorithms or a graph-theoretic methodology which makes use of the semantics information included in the Gene Ontology (GO) tree. The selected features, corresponding to methylation of CpG sites, attained moderate-to-high classification accuracies when imported to a series of classifiers evaluated by resampling or blindfold validation. The semantics-driven selection revealed sets of CpG sites performing similarly with evolutionary selection in the classification tasks. However, gene enrichment and pathway analysis showed that it additionally provides more descriptive sets of GO terms and KEGG pathways regarding the cancer phenotypes studied here. Results support the expediency of this methodology regarding its application in epidemiological studies. |
| Databáze: | OpenAIRE |
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