title: Parenclitic Network Analysis of Methylation Data for Cancer Identification.
abstract: We make use of ideas from the theory of complex networks to implement a machine learning classification of human DNA methylation data, that carry signatures of cancer development. The data were obtained from patients with various kinds of cancers and represented as parenclictic networks, wherein nodes correspond to genes, and edges are weighted according to pairwise variation from control group subjects. We demonstrate that for the 10 types of cancer under study, it is possible to obtain a high performance of binary classification between cancer-positive and negative samples based on network measures. Remarkably, an accuracy as high as 93-99% is achieved with only 12 network topology indices, in a dramatic reduction of complexity from the original 15295 gene methylation levels. Moreover, it was found that the parenclictic networks are scale-free in cancer-negative subjects, and deviate from the power-law node degree distribution in cancer. The node centrality ranking and arising modular structure could provide insights into the systems biology of cancer.
authors.affiliation: Department of Applied Mathematics and Centre of Bioinformatics, Lobachevsky State University of Nizhny Novgorod, Nizhniy Novgorod, Russia.
authors.forename: Alexander
authors.initials: A
authors.lastname: Karsakov
authors.affiliation: Institute for Women's Health and Department of Mathematics, University College London, London, United Kingdom.
authors.forename: Thomas
authors.initials: T
authors.lastname: Bartlett
authors.affiliation: Department of Applied Mathematics and Centre of Bioinformatics, Lobachevsky State University of Nizhny Novgorod, Nizhniy Novgorod, Russia.
authors.forename: Artem
authors.initials: A
authors.lastname: Ryblov
authors.affiliation: Department of Mathematical Software and High-Performance Computing, Lobachevsky State University of Nizhny Novgorod, Nizhniy Novgorod, Russia.
authors.forename: Iosif
authors.initials: I
authors.lastname: Meyerov
authors.affiliation: Department of Applied Mathematics and Centre of Bioinformatics, Lobachevsky State University of Nizhny Novgorod, Nizhniy Novgorod, Russia.
authors.forename: Mikhail
authors.initials: M
authors.lastname: Ivanchenko
authors.affiliation: Department of Applied Mathematics and Centre of Bioinformatics, Lobachevsky State University of Nizhny Novgorod, Nizhniy Novgorod, Russia.|Institute for Women's Health and Department of Mathematics, University College London, London, United Kingdom.
authors.forename: Alexey
authors.initials: A
authors.lastname: Zaikin
mesh_terms: D019175:DNA Methylation; D006801:Humans; D009369:Neoplasms / Q000175:diagnosis / Q000235:genetics*; D049490:Systems Biology