2020
DOI: 10.3390/cancers12082086
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Computational Identification of Gene Networks as a Biomarker of Neuroblastoma Risk

Abstract: Neuroblastoma is a common cancer in children, affected by a number of genes that interact with each other through intricate but coordinated networks. Traditional approaches can only reconstruct a single regulatory network that is topologically not informative enough to explain the complexity of neuroblastoma risk. We implemented and modified an advanced model for recovering informative, omnidirectional, dynamic, and personalized networks (idopNetworks) from static gene expression data for neuroblastoma risk. W… Show more

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Cited by 10 publications
(4 citation statements)
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“…Network analysis of various molecular and regulatory factors [ 49 , 50 ] is a strong and widely used approach for exploring the underlying mechanisms of any disease. It has not only been used in AML research [ 51 53 ] but also for several other diseases and physiological conditions [ 54 56 ]. In the current study, we constructed protein-protein interaction network and revealed the close relationships among the m5C regulators.…”
Section: Discussionmentioning
confidence: 99%
“…Network analysis of various molecular and regulatory factors [ 49 , 50 ] is a strong and widely used approach for exploring the underlying mechanisms of any disease. It has not only been used in AML research [ 51 53 ] but also for several other diseases and physiological conditions [ 54 56 ]. In the current study, we constructed protein-protein interaction network and revealed the close relationships among the m5C regulators.…”
Section: Discussionmentioning
confidence: 99%
“…Existing approaches for genomic studies mainly focus on the identification of individual genes, proteins, or metabolites that are associated with the severity of COVID-19, but they do not attempt to characterize how all these entities affect the disease as a cohesive whole through reconstructing interaction networks. We propose a new computational framework for inferring maximally informative, dynamic, omnidirectional, and personalized networks (idopNetworks) from expression data [ 33 , 75 ]. By implementing high-dimensional statistical theory and methods, this framework can reconstruct idopNetworks at any dimension from any high or even ultrahigh dimension of data.…”
Section: Discussionmentioning
confidence: 99%
“…In practice, the capacity of dynamic models is impaired by the availability of high‐density dynamic data whose collection is extremely expensive, unrealistic, or ethically impermissible (Xiao et al, 2017). To address this issue, a dynamic convertor has been invented to extract dynamic information from snapshots of static data by which dynamic models can be used (Chen et al, 2019; Sun et al, 2020; Wang et al, 2022; Wu & Jiang, 2021). By integrating evolutionary game theory and predator–prey theory using this dynamic convertor, a system of quasi‐dynamic ordinary differential equations (qdODE) has been derived to decompose the overall value of an element into its independent component due to its intrinsic capacity and dependent component due to the influence of other elements on it.…”
Section: Introductionmentioning
confidence: 99%