ArticlePloS one2017
Parenclitic Network Analysis of Methylation Data for Cancer Identification.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Epigenetic networks coordinate DNA methylation across the genome.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Review
- Prognosis and Survival Modelling in Cirrhosis Using Parenclitic Networks.Frontiers in network physiology · 2022Article
- Parenclitic and Synolytic Networks Revisited.Frontiers in genetics · 2021Article
- The Human Body as a Super Network: Digital Methods to Analyze the Propagation of Aging.Frontiers in aging neuroscience · 2020Article
- Algorithms designed for compressed-gene-data transformation among gene banks with different references.BMC bioinformatics · 2018Article
- Parenclitic networks for predicting ovarian cancer.Oncotarget · 2018Article
- Network spectra for drug-target identification in complex diseases: new guns against old foes.Applied network science · 2018Review
Corrections and comments
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Authors and funding
6 authors.
Funding
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.
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Registered trials
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