Structure and dynamics of human complication-disease network

Jiang, Xiong-Fei; Xiong, Long; Bai, Ling; Lin, Jie; Zhang, Jingfeng; Yan, Kun; Zhu, Jun; Zheng, Bin; Zheng, Jianjun

doi:10.1016/j.chaos.2022.112633

Chaos, Solitons & Fractals

2022

DOI: 10.1016/j.chaos.2022.112633

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Structure and dynamics of human complication-disease network

Xiong-Fei Jiang¹,

Long Xiong²,

Ling Bai³

et al.

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Cited by 2 publications

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References 56 publications

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“…This becomes even more significant, taking into account the ability of chromosome instability or genome chaos to produce pathological conditions (e.g. cancer and neurodegeneration) through the lifespan [25, [29][30][31][32]. Finally, the systems biology methodology (bioinformatics or systems genomics) [33,34], applied to cytogenomic and cytogenetic data should be efficient for uncovering genetic/genomic mechanisms of a variety of clinical conditions [10,28,35] leading to a possibility of treating diseases associated with chromosome imbalances, which are generally considered as incurable [36].…”

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“…Thus, a heuristic algorithm for comprehensive genome analysis for medical genetics does require both karyotyping (classical cytogenetics) and molecular karyotyping by SNP array. Since FISH allows single-cell monitoring of genomic/chromosomal variations, it is, thereby, required for cases of mosaicism and chromosome/genome instability Despite the studies reporting significantly increased diagnostic efficiency rates resulted from the application of array genome scanning techniques, understanding the mechanisms of the diseases and unraveling the cellular and molecular pathways requires a bioinformatic addition to the analytic workflows [16, 28,32,36,44,48]. Bioinformatic approaches to analyze data obtained by new sequencing technologies including panel versions were suggested to be an alternative [9,11,48,49].…”

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olecular cytogenetic and cytopostgenomic analysis of the human genome

Iourov¹,

Vorsanova²,

Куринная³

et al. 2022

RR. in B.

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Despite the achievements of human genomics, comprehensive genome analysis, including acquiring the knowledge about intercellular and interindividual variations at (sub)chromosomal/ cytogenomic level, remains a difficult task. This basically results from a lack of heuristic algorithms for uncovering (cyto)genomic and/or somatic genome variations and the functional outcomes. However, current developments in molecular cytogenetics and “cytopostgenomics” may offer a solution of the problem. The aim of the study: To present a heuristic algorithm for molecular cytogenetic and cytopostgenomic analysis of the human genome to uncover mechanisms of genetic (brain/neurodevelopmental) diseases. Materials and methods: Data on cytogenetic and (cyto)genomic variations (chromosome abnormalities, chromosome/genome instability, copy number variation (CNV) etc.) addressed by original molecular cytogenetic techniques and processed by original bioinformatic (cytopostgenomic) methods were used to develop the algorithm. Karyotyping was performed in 8556 individuals. FISH analysis was applied when required (cases of somatic mosaicism/ chromosome instability). Molecular karyotyping by SNP-array was performed in 600 (~7%) cases. Results: Using our long-term experience of studying chromosomal and genomic variations/instability in neurodevelopmental disorders as well as original developments in (cyto) genomic data processing, we managed to present a heuristic algorithm for molecular cytogenetic and cytopostgenomic analysis of the human genome to uncover mechanisms for brain diseases. Estimated efficiency of the algorithm was established to achieve 84%. Analyzing the dynamics of applying cytogenetic and cytogenomic techniques throughout ~35 years of our diagnostic research we found that the diagnostic efficiency had been increasing from ~7% (exclusive diagnosis by karyotyping) to more than 80% (molecular cytogenetic and cytopostgenomic analysis). Conclusion: Here, we propose a heuristic algorithm for molecular cytogenetic and cytopostgenomic analysis of the human genome to uncover mechanisms for genetic diseases. The efficiency and ability to uncover mechanisms of chromosome instability allows us to conclude that the algorithm may be highly competitive for basic and diagnostic genomic/cyto(post)genomic research.

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confidence: 99%

mentioning

confidence: 99%

olecular cytogenetic and cytopostgenomic analysis of the human genome

Iourov¹,

Vorsanova²,

Куринная³

et al. 2022

RR. in B.

View full text Add to dashboard Cite

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Searching for Heavy-Tailed Probability Distributions for Modeling Real-World Complex Networks

et al. 2022

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Perhaps the most recent controversial topic in network science research is to determine whether real-world complex networks are scale-free or not. Recently, Broido and Clauset [A.D. Broido, A. Clauset, Nature Communication, 10, 1017 (2019)] asserted that the degree distributions of real-world networks are rarely power law under statistical tests. Such complex networks, including social, biological, information, temporal, and brain networks, are often heavy-tailed where the assumption on the scale-free nature of real-world heavy-tailed networks become insignificant as the complex system evolves over time. The failure of power law distribution in fitting the degree distribution data is mainly due to the presence of an identifiable non-linearity within the entire degree distribution in a log-log scale of a complex heavytailed network. In this study, we attempt to address this issue by proposing a new class of heavy-tailed probability distributions for modeling the entire degree distributions of complex networks. We introduce a new family of generalized Lomax models (GLM) to capture the non-linearity of these heavy-tailed networks. These newly introduced GLM-type distributions provide better fitting and greater flexibility to the entire node degree distribution of complex networks. Several statistical properties of the proposed model, such as extreme value and inferential statistical properties, are derived into this context. Interestingly, the GLM family belongs to the basin of attraction of Frechet distribution, a heavy-tailed extreme value distribution. Rigorous experimental analysis showcases the excellent performance of the proposed family of distributions while fitting the heavy-tailed real-world complex networks over fifty real-world datasets in comparison with benchmark probability models. Our results show that GLM-type distributions are not rare, able to model almost 90% of the tested networks accurately compared to benchmark probability models.

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Structure and dynamics of human complication-disease network

Cited by 2 publications

References 56 publications

olecular cytogenetic and cytopostgenomic analysis of the human genome

olecular cytogenetic and cytopostgenomic analysis of the human genome

Searching for Heavy-Tailed Probability Distributions for Modeling Real-World Complex Networks

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