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From the third issue of 2017, Microarrays has changed its name to High-Throughput.

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Microarrays 2015, 4(4), 647-670; doi:10.3390/microarrays4040647

Cancer Biomarkers from Genome-Scale DNA Methylation: Comparison of Evolutionary and Semantic Analysis Methods

National Hellenic Research Foundation, Institute of Biology, Medicinal Chemistry and Biotechnology, 48 Vassileos Constantinou Avenue, 11635 Athens, Greece
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Academic Editor: Heather J. Ruskin
Received: 27 August 2015 / Revised: 9 November 2015 / Accepted: 18 November 2015 / Published: 27 November 2015
(This article belongs to the Special Issue Computational Modeling and Analysis of Microarray Data: New Horizons)
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Abstract

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. View Full-Text
Keywords: DNA methylation; breast cancer; B-cell lymphoma; epigenetic biomarker; classification; graph-theory; evolutionary algorithm; gene ontology tree DNA methylation; breast cancer; B-cell lymphoma; epigenetic biomarker; classification; graph-theory; evolutionary algorithm; gene ontology tree
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Valavanis, I.; Pilalis, E.; Georgiadis, P.; Kyrtopoulos, S.; Chatziioannou, A. Cancer Biomarkers from Genome-Scale DNA Methylation: Comparison of Evolutionary and Semantic Analysis Methods. Microarrays 2015, 4, 647-670.

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