scholarly journals Identification of Genetic Networks

Genetics ◽  
2004 ◽  
Vol 166 (2) ◽  
pp. 1037-1052
Author(s):  
Momiao Xiong ◽  
Jun Li ◽  
Xiangzhong Fang
Keyword(s):  
Differential Expression ◽  
Genetic Network ◽  
Structural Equations ◽  
Genetic Networks ◽  
Data Sets ◽  
Biological Processes ◽  
Expression Data ◽  
Gene Expression Data Analysis ◽  
Cell Phenotypes ◽  
Best Fit

Abstract In this report, we propose the use of structural equations as a tool for identifying and modeling genetic networks and genetic algorithms for searching the most likely genetic networks that best fit the data. After genetic networks are identified, it is fundamental to identify those networks influencing cell phenotypes. To accomplish this task we extend the concept of differential expression of the genes, widely used in gene expression data analysis, to genetic networks. We propose a definition for the differential expression of a genetic network and use the generalized T  2 statistic to measure the ability of genetic networks to distinguish different phenotypes. However, describing the differential expression of genetic networks is not enough for understanding biological systems because differences in the expression of genetic networks do not directly reflect regulatory strength between gene activities. Therefore, in this report we also introduce the concept of differentially regulated genetic networks, which has the potential to assess changes of gene regulation in response to perturbation in the environment and may provide new insights into the mechanism of diseases and biological processes. We propose five novel statistics to measure the differences in regulation of genetic networks. To illustrate the concepts and methods for reconstruction of genetic networks and identification of association of genetic networks with function, we applied the proposed models and algorithms to three data sets.

scholarly journals Extraction and Integration of Genetic Networks from Short-Profile Omic Data Sets

Metabolites ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 435
Author(s):  
Jacopo Iacovacci ◽  
Alina Peluso ◽  
Timothy Ebbels ◽  
Markus Ralser ◽  
Robert C. Glen
Keyword(s):  
High Throughput Screening ◽  
Model Organism ◽  
Similarity Measures ◽  
Genetic Network ◽  
Genetic Networks ◽  
Data Sets ◽  
Metabolomics Data ◽  
Molecular Features ◽  
Alternative Description ◽  
Non Gaussian

Mass spectrometry technologies are widely used in the fields of ionomics and metabolomics to simultaneously profile the intracellular concentrations of, e.g., amino acids or elements in genome-wide mutant libraries. These molecular or sub-molecular features are generally non-Gaussian and their covariance reveals patterns of correlations that reflect the system nature of the cell biochemistry and biology. Here, we introduce two similarity measures, the Mahalanobis cosine and the hybrid Mahalanobis cosine, that enforce information from the empirical covariance matrix of omics data from high-throughput screening and that can be used to quantify similarities between the profiled features of different mutants. We evaluate the performance of these similarity measures in the task of inferring and integrating genetic networks from short-profile ionomics/metabolomics data through an analysis of experimental data sets related to the ionome and the metabolome of the model organism S. cerevisiae. The study of the resulting ionome–metabolome Saccharomyces cerevisiae multilayer genetic network, which encodes multiple omic-specific levels of correlations between genes, shows that the proposed measures can provide an alternative description of relations between biological processes when compared to the commonly used Pearson’s correlation coefficient and have the potential to guide the construction of novel hypotheses on the function of uncharacterised genes.

scholarly journals An Approach to Inferring Transcriptional Regulation Among Genes From Large-Scale Expression Data

2003 ◽  
Vol 4 (1) ◽  
pp. 148-154 ◽  
Author(s):  
Javier Herrero ◽  
Ramón Díaz-Uriarte ◽  
Joaquín Dopazo
Keyword(s):  
Large Scale ◽  
Time Course ◽  
Dna Microarrays ◽  
Genetic Network ◽  
Genetic Networks ◽  
Gene Interactions ◽  
Expression Data ◽  
Dynamic Correlations ◽  
Common Time ◽  
Data Points

The use of DNA microarrays opens up the possibility of measuring the expression levels of thousands of genes simultaneously under different conditions. Time-course experiments allow researchers to study the dynamics of gene interactions. The inference of genetic networks from such measures can give important insights for the understanding of a variety of biological problems. Most of the existing methods for genetic network reconstruction require many experimental data points, or can only be applied to the reconstruction of small subnetworks. Here we present a method that reduces the dimensionality of the dataset and then extracts the significant dynamic correlations among genes. The method requires a number of points achievable in common time-course experiments.

scholarly journals Signatures and Prognostic Values of Related Immune Targets in Tongue Cancer

2021 ◽  
Author(s):  
Xi Yu ◽  
Xiaofei Lv
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Tongue Cancer ◽  
Differential Expression Analysis ◽  
Marker Genes ◽  
Data Sets ◽  
Expression Data ◽  
Oral Cancers ◽  
Limma Package ◽  
Cancer Bioinformatics

Abstract Tongue cancer, as one of the most malignant oral cancers, is highly invasive and has a high risk of recurrence. At present, tongue cancer in the advanced stage is not obvious, easy to miss the opportunity of early diagnosis. It is important to find markers that can predict the occurrence and progression of tongue cancer. Bioinformatics analysis plays an important role in the acquisition of marker genes. GEO and TCGA data are very important public databases. In addition to expression data, TCGA database also contains corresponding clinical data. In this study, we screened three GEO datasets included GSE13601, GSE34105 and GSE34106 that met the standard. These data sets were combined using the SVA package to prepare the data for differential expression analysis, and then the LIMMA package was used to set the standard to p<0.05 and |log2 (FC)| ≥1.5. We got 170 DEGs (104, raised 66 downgrade). Besides, the DEseq package was used for differential expression analysis using the same criteria for samples in TCGA database. It ended up with 1589 DEGs (644 up-regulated, 945 down-regulated). By merging these two sets of DEGs, 5 common up-regulated DEGs (CCL20, SCG5, SPP1, KRT75 and FOLR3) and 15 common down-regulated DEGs were obtained. Further functional analysis of the DEGs showed that CCL20, SCG5 and SPP1 is closely related to prognosis and may be a therapeutic target of TSCC.

scholarly journals Integrative Genomics of the Mammalian Alveolar Macrophage Response to Intracellular Mycobacteria

2020 ◽  
Author(s):  
Thomas J. Hall ◽  
Michael P. Mullen ◽  
Gillian P. McHugo ◽  
Kate E. Killick ◽  
Siobhán C. Ring ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alveolar Macrophage ◽  
Differential Expression ◽  
Gene Expression Data ◽  
Host Response ◽  
Gwas Data ◽  
Data Sets ◽  
Expression Data ◽  
Rna Seq ◽  
Time Point

Abstract BackgroundBovine TB (BTB), caused by infection with Mycobacterium bovis, is a major endemic disease affecting global cattle production, particularly in many developing countries. The key innate immune that first encounters the pathogen is the alveolar macrophage, previously shown to be substantially reprogrammed during intracellular infection by the pathogen. Here we use differential expression, and correlation- and interaction-based network approaches to analyse the host response to infection with M. bovis at the transcriptome level to identify core infection response pathways and gene modules. These outputs were then integrated with genome-wide association study (GWAS) data sets to enhance detection of genomic variants for susceptibility/resistance to M. bovis infection.ResultsThe host gene expression data consisted of bovine RNA-seq data from alveolar macrophages infected with M. bovis at 24 and 48 hours post-infection. These RNA-seq data were analysed using three distinct analysis pipelines and novel response pathways and modules were further refined using cross-comparison and integration of the results. First, a differential expression analysis was carried out to determine the most significantly differentially expressed (DE) genes between conditions at each time point. Second, two networks were constructed at each time point using gene correlation patterns to determine changes in expression across conditions. Functional sub-modules within each correlation network were selected by statistical criteria for modularity. Third, a base gene interaction network of the mammalian host response to mycobacterial infection was generated using the GeneCards database and InnateDB. Differential gene expression data were superimposed on this base network to extract functional modules of interconnected DE genes.ConclusionsBovine GWAS data was obtained from a published BTB susceptibility/resistance study. The results from the three parallel analyses were integrated with this data to determine which of the three approaches identified genes significantly enriched for SNPs associated with susceptibility/resistance to M. bovis infection. Results indicate distinct and significant overlap in SNP discovery, demonstrating that network-based integration of biologically relevant transcriptomics data can leverage substantial additional information from GWAS data sets.

Cancer Gene Expression Data Analysis Using Rough Based Symmetrical Clustering

2013 ◽  
pp. 1626-1641
Author(s):  
Anasua Sarkar ◽  
Ujjwal Maulik
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Gene Expression Data ◽  
Rough Set ◽  
Clustering Algorithm ◽  
Data Sets ◽  
Cancer Gene ◽  
Expression Data ◽  
Gene Expression Data Analysis ◽  
Cancer Subtypes

Identification of cancer subtypes is the central goal in the cancer gene expression data analysis. Modified symmetry-based clustering is an unsupervised learning technique for detecting symmetrical convex or non-convex shaped clusters. To enable fast automatic clustering of cancer tissues (samples), in this chapter, the authors propose a rough set based hybrid approach for modified symmetry-based clustering algorithm. A natural basis for analyzing gene expression data using the symmetry-based algorithm is to group together genes with similar symmetrical patterns of microarray expressions. Rough-set theory helps in faster convergence and initial automatic optimal classification, thereby solving the problem of unknown knowledge of number of clusters in gene expression measurement data. For rough-set-theoretic decision rule generation, each cluster is classified using heuristically searched optimal reducts to overcome overlapping cluster problem. The rough modified symmetry-based clustering algorithm is compared with another newly implemented rough-improved symmetry-based clustering algorithm and existing K-Means algorithm over five benchmark cancer gene expression data sets, to demonstrate its superiority in terms of validity. The statistical analyses are also performed to establish the significance of this rough modified symmetry-based clustering approach.

Cancer Gene Expression Data Analysis Using Rough Based Symmetrical Clustering

2013 ◽  
pp. 699-715 ◽  
Author(s):  
Anasua Sarkar ◽  
Ujjwal Maulik
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Gene Expression Data ◽  
Rough Set ◽  
Clustering Algorithm ◽  
Data Sets ◽  
Cancer Gene ◽  
Expression Data ◽  
Gene Expression Data Analysis ◽  
Cancer Subtypes

Identification of cancer subtypes is the central goal in the cancer gene expression data analysis. Modified symmetry-based clustering is an unsupervised learning technique for detecting symmetrical convex or non-convex shaped clusters. To enable fast automatic clustering of cancer tissues (samples), in this chapter, the authors propose a rough set based hybrid approach for modified symmetry-based clustering algorithm. A natural basis for analyzing gene expression data using the symmetry-based algorithm is to group together genes with similar symmetrical patterns of microarray expressions. Rough-set theory helps in faster convergence and initial automatic optimal classification, thereby solving the problem of unknown knowledge of number of clusters in gene expression measurement data. For rough-set-theoretic decision rule generation, each cluster is classified using heuristically searched optimal reducts to overcome overlapping cluster problem. The rough modified symmetry-based clustering algorithm is compared with another newly implemented rough-improved symmetry-based clustering algorithm and existing K-Means algorithm over five benchmark cancer gene expression data sets, to demonstrate its superiority in terms of validity. The statistical analyses are also performed to establish the significance of this rough modified symmetry-based clustering approach.

scholarly journals Cancer Genetic Network Inference Using Gaussian Graphical Models

2019 ◽  
Vol 13 ◽  
pp. 117793221983940 ◽  
Author(s):  
Haitao Zhao ◽  
Zhong-Hui Duan
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Human Cancer ◽  
Genetic Network ◽  
Genetic Networks ◽  
Gene Interactions ◽  
Expression Data ◽  
Rna Seq ◽  
Cancer Genetic ◽  
Graphical Lasso

The Cancer Genome Atlas (TCGA) provides a rich resource that can be used to understand how genes interact in cancer cells and has collected RNA-Seq gene expression data for many types of human cancer. However, mining the data to uncover the hidden gene-interaction patterns remains a challenge. Gaussian graphical model (GGM) is often used to learn genetic networks because it defines an undirected graphical structure, revealing the conditional dependences of genes. In this study, we focus on inferring gene interactions in 15 specific types of human cancer using RNA-Seq expression data and GGM with graphical lasso. We take advantage of the corresponding Kyoto Encyclopedia of Genes and Genomes pathway maps to define the subsets of related genes. RNA-Seq expression levels of the subsets of genes in solid cancerous tumor and normal tissues were extracted from TCGA. The gene expression data sets were cleaned and formatted, and the genetic network corresponding to each cancer type was then inferred using GGM with graphical lasso. The inferred networks reveal stable conditional dependences among the genes at the expression level and confirm the essential roles played by the genes that encode proteins involved in the two key signaling pathway phosphoinositide 3-kinase (PI3K)/AKT/mTOR and Ras/Raf/MEK/ERK in human carcinogenesis. These stable dependences elucidate the expression level interactions among the genes that are implicated in many different human cancers. The inferred genetic networks were examined to further identify and characterize a collection of gene interactions that are unique to cancer. The cross-cancer genetic interactions revealed from our study provide another set of knowledge for cancer biologists to propose strong hypotheses, so further biological investigations can be conducted effectively.

scholarly journals Consistency of nonlinear interacting ghost dark energy with recent observations

2017 ◽  
Vol 26 (11) ◽  
pp. 1750124 ◽  
Author(s):  
E. Ebrahimi ◽  
H. Golchin ◽  
A. Mehrabi ◽  
S. M. S. Movahed
Keyword(s):  
Dark Energy ◽  
Observational Data ◽  
Nonlinear Interaction ◽  
Acoustic Oscillation ◽  
Joint Analysis ◽  
Data Sets ◽  
Dark Energy Density ◽  
Ghost Dark Energy ◽  
Interaction Terms ◽  
Best Fit

In this paper, we investigate ghost dark energy model in the presence of nonlinear interaction between dark energy and dark matter. We also extend the analysis to the so-called generalized ghost dark energy (GGDE) which [Formula: see text]. The model contains three free parameters as [Formula: see text] and [Formula: see text] (the coupling coefficient of interactions). We propose three kinds of nonlinear interaction terms and discuss the behavior of equation of state, deceleration and dark energy density parameters of the model. We also find the squared sound speed and search for signs of stability of the model. To compare the interacting GGDE model with observational data sets, we use more recent observational outcomes, namely SNIa from JLA catalog, Hubble parameter, baryonic acoustic oscillation and the most relevant CMB parameters including, the position of acoustic peaks, shift parameters and redshift to recombination. For GGDE with the first nonlinear interaction, the joint analysis indicates that [Formula: see text], [Formula: see text] and [Formula: see text] at 1 optimal variance error. For the second interaction, the best fit values at [Formula: see text] confidence are [Formula: see text], [Formula: see text] and [Formula: see text]. According to combination of all observational data sets considered in this paper, the best fit values for third nonlinearly interacting model are [Formula: see text], [Formula: see text] and [Formula: see text] at [Formula: see text] confidence interval. Finally, we found that the presence of interaction is compatible in mentioned models via current observational datasets.

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