ArticleJournal of the Royal Society, Interface2018
Understanding diseases as increased heterogeneity: a complex network computational framework.
Article in Journal of the Royal Society, Interface, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
3 citing papers in PubMed.
- Single-Sample Networks Reveal Intra-Cytoband Co-Expression Hotspots in Breast Cancer Subtypes.International journal of molecular sciences · 2024Article
- Unraveling patient heterogeneity in complex diseases through individualized co-expression networks: a perspective.Frontiers in genetics · 2023Article
- DNA Methylation Analysis of Ribosomal DNA in Adults With Down Syndrome.Frontiers in genetics · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Owing to the complexity of the human body, most diseases present a high interpersonal variability in the way they manifest, i.e. in their phenotype, which has important clinical repercussions-for instance, the difficulty in defining objective diagnostic rules. Here we explore the hypothesis that signs and symptoms used to define a disease should be understood in terms of the dispersion (as opposed to the average) of physical observables. To that end, we propose a computational framework, based on complex networks theory, to map groups of subjects to a network structure, based on their pairwise phenotypical similarity. We demonstrate that the resulting structure can be used to improve the performance of classification algorithms, especially in the case of a limited number of instances, with both synthetic and real datasets. Beyond providing an alternative conceptual understanding of diseases, the proposed framework could be of special relevance in the growing field of personalized, or
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Registered trials
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