Multivariate Entropy Characterizes the Gene Expression and Protein-Protein Networks in Four Types of Cancer

Juarez-Flores, Angel; José, Marco V.

doi:10.3390/e20030154

Cited by 4 publications

(3 citation statements)

References 48 publications

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“…Banerji et al [276] suggested that signaling entropy provided a potential measure in cancer by investigating microarray gene expression data in 3668 breast cancer samples and 1692 lung adenocarcinoma samples. Juarez-Flores and José [277] also detected the sudden changes in entropy values in four cancer types and identified the most relevant genes involved in carcinogenic processes that would be potential biomarkers or therapeutic targets. Intriguingly, Teschendorff et al [278] found that increased network entropy of cancer cells would be driven by the scale-free (or near scale-free) property of the interaction network and the positive correlation between differential gene expression and node degree.…”

Section: Network Entropymentioning

confidence: 98%

Computational network biology: Data, models, and applications

et al. 2020

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Biological entities are involved in intricate and complex interactions, in which uncovering the biological information from the network concepts are of great significance. Benefiting from the advances of network science and high-throughput biomedical technologies, studying the biological systems from network biology has attracted much attention in recent years, and networks have long been central to our understanding of biological systems, in the form of linkage maps among genotypes, phenotypes, and the corresponding environmental factors. In this review, we summarize the recent developments of computational network biology, first introducing various types of biological networks and network structural properties. We then review the network-based approaches, ranging from some network metrics to the complicated machine-learning methods, and emphasize how to use these algorithms to gain new biological insights. Furthermore, we highlight the application in neuroscience, human disease, and drug developments from the perspectives of network science, and we discuss some major challenges and future directions. We hope that this review will draw increasing

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Section: Network Entropymentioning

confidence: 98%

Computational network biology: Data, models, and applications

et al. 2020

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“…There are different metrics that can be obtained from networks as the hubs which are commonly referred as the most connected nodes which lead to network instability if they are perturbed 10 – 13 . Besides, other network measures as betweenness and multivariate entropy have been used to analyze cancer networks to find putative potential targets for cancer disease 14 , 15 . We previously identified a set of nodes which due to its biological and network properties we called them network gatekeepers 16 .…”

Section: Introductionmentioning

confidence: 99%

Novel gene signatures for stage classification of the squamous cell carcinoma of the lung

Juarez-Flores

Zamudio

José

2021

Sci Rep

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The squamous cell carcinoma of the lung (SCLC) is one of the most common types of lung cancer. As GLOBOCAN reported in 2018, lung cancer was the first cause of death and new cases by cancer worldwide. Typically, diagnosis is made in the later stages of the disease with few treatment options available. The goal of this work was to find some key components underlying each stage of the disease, to help in the classification of tumor samples, and to increase the available options for experimental assays and molecular targets that could be used in treatment development. We employed two approaches. The first was based in the classic method of differential gene expression analysis, network analysis, and a novel concept known as network gatekeepers. The second approach was using machine learning algorithms. From our combined approach, we identified two sets of genes that could function as a signature to identify each stage of the cancer pathology. We also arrived at a network of 55 nodes, which according to their biological functions, they can be regarded as drivers in this cancer. Although biological experiments are necessary for their validation, we proposed that all these genes could be used for cancer development treatments.

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“…In the area of biology, they have been used to analyze a population's structure [2,3], and pandemics [4,5]. The use of protein-protein interaction networks coupled with information theory have led to discover potential therapeutic biomarkers on cancer research [6]. Integrative approaches for anticipating critical transitions have been proposed [7] in several phenomena, although the area of cardiology has not yet been explored.…”

Section: Introductionmentioning

confidence: 99%

Anticipation of ventricular tachyarrhythmias by a novel mathematical method: Further insights towards an early warning system in implantable cardioverter defibrillators

Zamudio¹,

Márquez²,

José³

2020

PLoS ONE

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Implantable cardioverter defibrillators (ICD) are the most effective therapy to terminate malignant ventricular arrhythmias (VA) and therefore to prevent sudden cardiac death. Until today, there is no way to predict the onset of such VA. Our aim was to develop a mathematical model that could predict VA in a timely fashion. We analyzed the time series of R-R intervals from 3 groups. Two groups from the Spontaneous Ventricular Tachyarrhythmia Database (v 1.0) were analyzed from a set of 81 pairs of R-R interval time series records from patients, each pair containing one record before the VT episode (Dataset 1A) and one control record which was obtained during the follow up visit (Dataset 1B). A third data set was composed of the R-R interval time series of 54 subjects without a significant arrhythmia heart disease (Dataset 2). We developed a new method to transform a time series into a network for its analysis, the ε−regular graphs. This novel approach transforms a time series into a network which is sensitive to the quantitative properties of the time series, it has a single parameter (ε) to be adjusted, and it can trace long-range correlations. This procedure allows to use graph theory to extract the dynamics of any time series. The average of the difference between the VT and the control record graph degree of each patient, at each time window, reached a global minimum value of −2.12 followed by a drastic increase of the average graph until reaching a local maximum of 5.59. The global minimum and the following local maxima occur at the windows 276 and 393, respectively. This change in the connectivity of the graphs distinguishes two distinct dynamics occurring during the VA, while the states in between the 276 and 393, determine a transitional state. We propose this change in the dynamic of the R-R intervals as a measurable and detectable "early warning" of the VT event, occurring an average of 514.625 seconds (8:30 minutes) before the onset of the VT episode. It is feasible to detect retrospectively early warnings of the VA episode using their corresponding ε−regular graphs, with an average of 8:30 minutes before the ICD terminates the VA event.

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Multivariate Entropy Characterizes the Gene Expression and Protein-Protein Networks in Four Types of Cancer

Cited by 4 publications

References 48 publications

Computational network biology: Data, models, and applications

Computational network biology: Data, models, and applications

Novel gene signatures for stage classification of the squamous cell carcinoma of the lung

Anticipation of ventricular tachyarrhythmias by a novel mathematical method: Further insights towards an early warning system in implantable cardioverter defibrillators

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