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.
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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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