scholarly journals Analysis and Research of Key Genes in Gene Expression Network Based on Complex Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Guobin Chen ◽  
Jun Qi ◽  
Chao Tang ◽  
Ying Wang ◽  
Yongzhong Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Networks ◽  
Complex Network ◽  
Network Structure ◽  
Gene Networks ◽  
Gene Network ◽  
Gene Expression Network ◽  
Pagerank Algorithm ◽  
Key Genes ◽  
Gene Data

Gene expression network is also a type of complex network. It is challenging to analyze the gene expression network through relevant knowledge and algorithms of a complex network. In this paper, the existing characteristics of genes are analyzed from various indexes of the gene expression network to analyze key genes and TOP genes. Firstly, gene chip data are screened, gene data with obvious characteristics are selected, and relevant clustering characteristics are analyzed. Then, the complex gene network structure is established, and gene networks with different threshold shapes and different sizes are selected. Finally, the relevant indexes and PR values after the PageRank algorithm are analyzed for complex networks under different thresholds, thus establishing the TOP gene and PR sequence.

EFFECTIVE MODELS FOR GENE NETWORKS AND THEIR APPLICATIONS

2012 ◽  
Vol 07 (01n02) ◽  
pp. 41-70 ◽  
Author(s):  
JASON SHULMAN ◽  
LARS SEEMANN ◽  
GREGG W. ROMAN ◽  
GEMUNU H. GUNARATNE
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Gene Network ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Stationary Solutions ◽  
The State ◽  
Effective Model ◽  
Knockout Mutant ◽  
Double Knockout

Networks are used to abstract large, highly-coupled sets of objects. Their analyses have included network classification into a few broad classes and selection of small substructures that perform simple yet common tasks. One issue that has received little attention is how the state of a network can be moved according to a pre-specified set of guidelines. In this paper, we address this question in the context of gene networks. In general, neither the full membership of the gene network associated with a biological process nor the precise form of interactions between nodes is known. What is available, through microarrays or sequencing, are gene expression profiles of an organism or its viable mutants. Our approach relies only on these expression profiles, and not on the availability of an accurate model for the network. The first step is to select a small set of core- or master- nodes, such as transcription factors or microRNAs, that can be used to alter the levels of many of the remaining genes in the network. We ask how the state — or solution — of the gene network changes as the levels of these master nodes are altered externally. The object of our study is, not the network, but the surface of these solutions. We argue that it can be approximated using gene expression profiles of the organism and single manipulation of master node activity. This is done through an "effective model." The effective model as well as error estimates for its predictions can be derived from experimental data. The method is validated using synthetic gene networks that have stationary solutions and those that are periodically driven, e.g., circadian networks. An effective model for the oxygen-deprivation network of E.coli is constructed using previously published gene expression profiles, and used to predict the expression levels in a double knockout mutant. Less that 30% of the predictions lie outside the 5% confidence level. We propose the use of the effective model methodology to compute how Drosophila melanogaster in the normal state can be genetically altered into a pre-defined sleep deprived-like state.

scholarly journals corto: a lightweight R package for Gene Network Inference and Master Regulator Analysis

2020 ◽  
Author(s):  
Daniele Mercatelli ◽  
Gonzalo Lopez-Garcia ◽  
Federico M. Giorgi
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Gene Network ◽  
Network Inference ◽  
Human Tumor ◽  
R Package ◽  
Specific Gene ◽  
Master Regulator ◽  
Gene Network Inference ◽  
Link Type

AbstractMotivationGene Network Inference and Master Regulator Analysis (MRA) have been widely adopted to define specific transcriptional perturbations from gene expression signatures. Several tools exist to perform such analyses, but most require a computer cluster or large amounts of RAM to be executed.ResultsWe developed corto, a fast and lightweight R package to infer gene networks and perform MRA from gene expression data, with optional corrections for Copy Number Variations (CNVs) and able to run on signatures generated from RNA-Seq or ATAC-Seq data. We extensively benchmarked it to infer context-specific gene networks in 39 human tumor and 27 normal tissue datasets.AvailabilityCross-platform and multi-threaded R package on CRAN (stable version) https://cran.rproject.org/package=corto and Github (development release) https://github.com/federicogiorgi/[email protected]

scholarly journals Neural Gene Network Constructor: A Neural Based Model for Reconstructing Gene Regulatory Network

2019 ◽  
Author(s):  
Zhang Zhang ◽  
Lifei Wang ◽  
Shuo Wang ◽  
Ruyi Tao ◽  
Jingshu Xiao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Network Structure ◽  
Regulatory Network ◽  
Gene Network ◽  
Regulatory Networks ◽  
Expression Data ◽  
Gene Regulatory

SummaryReconstructing gene regulatory networks (GRNs) and inferring the gene dynamics are important to understand the behavior and the fate of the normal and abnormal cells. Gene regulatory networks could be reconstructed by experimental methods or from gene expression data. Recent advances in Single Cell RNA sequencing technology and the computational method to reconstruct trajectory have generated huge scRNA-seq data tagged with additional time labels. Here, we present a deep learning model “Neural Gene Network Constructor” (NGNC), for inferring gene regulatory network and reconstructing the gene dynamics simultaneously from time series gene expression data. NGNC is a model-free heterogenous model, which can reconstruct any network structure and non-linear dynamics. It consists of two parts: a network generator which incorporating gumbel softmax technique to generate candidate network structure, and a dynamics learner which adopting multiple feedforward neural networks to predict the dynamics. We compare our model with other well-known frameworks on the data set generated by GeneNetWeaver, and achieve the state of the arts results both on network reconstruction and dynamics learning.

scholarly journals sgnesR: An R package for simulating gene expression data from an underlying real gene network structure considering delay parameters

2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Shailesh Tripathi ◽  
Jason Lloyd-Price ◽  
Andre Ribeiro ◽  
Olli Yli-Harja ◽  
Matthias Dehmer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Network Structure ◽  
Gene Network ◽  
R Package ◽  
Expression Data ◽  
Real Gene

scholarly journals Understanding diseases as increased heterogeneity: a complex network computational framework

2018 ◽  
Vol 15 (145) ◽  
pp. 20180405 ◽  
Author(s):  
Massimiliano Zanin ◽  
Juan Manuel Tuñas ◽  
Ernestina Menasalvas
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Conceptual Understanding ◽  
Network Structure ◽  
Human Body ◽  
Signs And Symptoms ◽  
Classification Algorithms ◽  
Computational Framework ◽  
Special Relevance ◽  
The Way

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 N -to-1, medicine.

Estimating Immunoregulatory Gene Networks in Human Herpesvirus Type 6 (HHV-6)-Infected T Cells.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 2381-2381
Author(s):  
Tomoiku Takaku ◽  
Junko H. Ohyashiki ◽  
Yu Zhang ◽  
Kazuma Ohyashiki
Keyword(s):  
Gene Expression ◽  
Immune Response ◽  
T Cells ◽  
Dna Microarray ◽  
Gene Networks ◽  
Gene Network ◽  
Human Herpesvirus ◽  
Host Immune Response ◽  
Herpesvirus Type ◽  
Human Herpesvirus Type

Abstract The immune response to viral infection involves complex network of dynamic gene and protein interactions. Comprehensive gene expression analysis of the host immune response against viruses has been extensively studied, however, the mechanism of virus-induced immune response is not completely understood. This might be due in part to the difficulty of finding pathologically relevant genes, despite the fact that DNA microarray technology can simultaneously monitor the expression of thousands of genes. Likewise, it is hard to estimate how each gene interferes during the viral infection. Thus, construction of gene networks from microarray gene expression data is becoming an important challenge in the post-genome era. Human herpesvirus 6 (HHV-6) is a β-herpesvirus that is closely related to human cytomegalovirus. HHV-6 shows a predominant tropism for CD4 T lymphocytes, on which it exerts marked cytopathic effects. Understanding of the clinical spectrum of HHV-6 is still evolving, however, in vitro interactions between HHV-6 and other viruses, such as the human immunodeficiency virus (HIV), and their relevance to the in vivo situation has become increasingly apparent. We present here the dynamic gene network of the host immune response during human herpesvirus type 6 (HHV-6) infection in an adult T cell leukemia (ATL) cell line. Using a pathway-focused oligonucleotide DNA microarray, we found a possible association between chemokine genes regulating Th1/Th2 balance and genes regulating T-cell proliferation during HHV-6B infection. Gene network analysis using an integrated comprehensive workbench, VoyaGene® revealed that a gene encoding a TEC-family kinase, ITK, might be a putative modulator in the host immune response against HHV-6B infection. We conclude that Th2-dominated inflammatory reaction in host cells may play an important role in HHV-6B infected T cells, thereby suggesting the possibility that ITK might be a therapeutic target in diseases related to dysregulation of Th1/Th2 balance. This study describes a novel approach to find genes related with the complex host-virus interaction using microarray data employing the Bayesian statistical framework. Figure Figure

scholarly journals Gene Network Exploration of Crosstalk between Apoptosis and Autophagy in Chronic Myelogenous Leukemia

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Fengfeng Wang ◽  
William C. S. Cho ◽  
Lawrence W. C. Chan ◽  
S. C. Cesar Wong ◽  
Nancy B. Y. Tsui ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Gene Network ◽  
Expression Profiles ◽  
Microarray Dataset ◽  
Underlying Mechanism ◽  
Gene Expression Omnibus ◽  
Myelogenous Leukemia ◽  
Underlying Mechanisms ◽  
Graphical Illustration

Background. Gene expression levels change to adapt the stress, such as starvation, toxin, and radiation. The changes are signals transmitted through molecular interactions, eventually leading to two cellular fates, apoptosis and autophagy. Due to genetic variations, the signals may not be effectively transmitted to modulate apoptotic and autophagic responses. Such aberrant modulation may lead to carcinogenesis and drug resistance. The balance between apoptosis and autophagy becomes very crucial in coping with the stress. Though there have been evidences illustrating the apoptosis-autophagy interplay, the underlying mechanism and the participation of the regulators including transcription factors (TFs) and microRNAs (miRNAs) remain unclear.Results. Gene network is a graphical illustration for exploring the functional linkages and the potential coordinate regulations of genes. Microarray dataset for the study of chronic myeloid leukemia was obtained from Gene Expression Omnibus. The expression profiles of those genes related to apoptosis and autophagy, including MCL1, BCL2, ATG, beclin-1, BAX, BAK, E2F, cMYC, PI3K, AKT, BAD, and LC3, were extracted from the dataset to construct the gene networks.Conclusion. The network analysis of these genes explored the underlying mechanisms and the roles of TFs and miRNAs for the crosstalk between apoptosis and autophagy.

scholarly journals corto: a lightweight R package for gene network inference and master regulator analysis

2020 ◽  
Vol 36 (12) ◽  
pp. 3916-3917 ◽  
Author(s):  
Daniele Mercatelli ◽  
Gonzalo Lopez-Garcia ◽  
Federico M Giorgi
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Gene Network ◽  
Network Inference ◽  
Human Tumor ◽  
R Package ◽  
Supplementary Information ◽  
Specific Gene ◽  
Master Regulator ◽  
Gene Network Inference

Abstract Motivation Gene network inference and master regulator analysis (MRA) have been widely adopted to define specific transcriptional perturbations from gene expression signatures. Several tools exist to perform such analyses but most require a computer cluster or large amounts of RAM to be executed. Results We developed corto, a fast and lightweight R package to infer gene networks and perform MRA from gene expression data, with optional corrections for copy-number variations and able to run on signatures generated from RNA-Seq or ATAC-Seq data. We extensively benchmarked it to infer context-specific gene networks in 39 human tumor and 27 normal tissue datasets. Availability and implementation Cross-platform and multi-threaded R package on CRAN (stable version) https://cran.r-project.org/package=corto and Github (development release) https://github.com/federicogiorgi/corto. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Evidence of Highly Regulated Genes (in-Hubs) in Gene Networks of Saccharomyces Cerevisiae

2008 ◽  
Vol 2 ◽  
pp. BBI.S853 ◽  
Author(s):  
Jesper Lundström ◽  
Johan Björkegren ◽  
Jesper Tegnér
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Gene Network ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Repeated Measurements ◽  
Experimental Conditions ◽  
Cellular Processes ◽  
Binding Data ◽  
External Stimuli

Uncovering interactions between genes, gene networks, is important to increase our understanding of intrinsic cellular processes and responses to external stimuli such as drugs. Gene networks can be computationally inferred from repeated measurements of gene expression, using algorithms, which assume that each gene is controlled by only a small number of other proteins. Here, by extending the transcription network with cofactors (defined from protein-protein binding data) as active regulators, we identified the effective gene network, providing evidence of in-hubs in the gene regulatory networks of yeast. Then, using the notion that in-hub genes will be differentially expressed over several experimental conditions, we designed an algorithm, the HubDetector, enabling identification of in-hubs directly from gene expression data. Applying the HubDetector to 488 genome-wide expression profiles from two independent datasets, we identified putative in-hubs overlapping significantly with in-hubs in the effective gene network.

scholarly journals Gap-com: General model selection criterion for sparse undirected gene networks with non-trivial community structure

2021 ◽  
Author(s):  
Markku Kuismin ◽  
Fatemeh Dodangeh ◽  
Mikko J Sillanpää
Keyword(s):  
Model Selection ◽  
Graphical Models ◽  
Network Structure ◽  
Gene Networks ◽  
Gene Network ◽  
Graphical Model ◽  
Regularization Parameter ◽  
Selection Criterion ◽  
Biological Data ◽  
Model Selection Criterion

Abstract We introduce a new model selection criterion for sparse complex gene network modeling where gene co-expression relationships are estimated from data. This is a novel formulation of the gap statistic and it can be used for the optimal choice of a regularization parameter in graphical models. Our criterion favors gene network structure which differs from a trivial gene interaction structure obtained totally at random. We call the criterion the gap-com statistic (gap community statistic). The idea of the gap-com statistic is to examine the difference between the observed and the expected counts of communities (clusters) where the expected counts are evaluated using either data permutations or reference graph (the Erdős-Rényi graph) resampling. The latter represents a trivial gene network structure determined by chance. We put emphasis on complex network inference because the structure of gene networks is usually non-trivial. For example, some of the genes can be clustered together or some genes can be hub genes. We evaluate the performance of the gap-com statistic in graphical model selection and compare its performance to some existing methods using simulated and real biological data example.

Sign in / Sign up

Export Citation Format

Share Document