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Parenclitic Network Analysis of Methylation Data for Cancer Identification.

التفاصيل البيبلوغرافية
العنوان: Parenclitic Network Analysis of Methylation Data for Cancer Identification.
المؤلفون: Alexander Karsakov, Thomas Bartlett, Artem Ryblov, Iosif Meyerov, Mikhail Ivanchenko, Alexey Zaikin
المصدر: PLoS ONE, Vol 12, Iss 1, p e0169661 (2017)
بيانات النشر: Public Library of Science (PLoS), 2017.
سنة النشر: 2017
المجموعة: LCC:Medicine
LCC:Science
مصطلحات موضوعية: Medicine, Science
الوصف: 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.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1932-6203
Relation: https://doaj.org/toc/1932-6203
DOI: 10.1371/journal.pone.0169661
URL الوصول: https://doaj.org/article/a318414744bd47f5b4a48a3802454048
رقم الأكسشن: edsdoj.318414744bd47f5b4a48a3802454048
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:19326203
DOI:10.1371/journal.pone.0169661