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Parenclitic network analysis of methylation data for cancer identification

Karsakov, A; Bartlett, T; Meyerov, I; Zaikin, A; Ivanchenko, M; (2017) Parenclitic network analysis of methylation data for cancer identification. PLOS One , 12 (1) , Article e0169661. 10.1371/journal.pone.0169661. Green open access

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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.

Type: Article
Title: Parenclitic network analysis of methylation data for cancer identification
Open access status: An open access version is available from UCL Discovery
DOI: 10.1371/journal.pone.0169661
Publisher version: http://dx.doi.org/10.1371/journal.pone.0169661
Language: English
Additional information: © 2017 Karsakov et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Keywords: Methylation, Genetic networks, Graphs, Centrality, DNA methylation, Linear regression analysis, Machine learning algorithms, Head and neck squamous cell carcinoma
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health > Womens Cancer
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/1500668
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