USING GRAPHICAL MODELS AND GENOMIC EXPRESSION DATA TO STATISTICALLY VALIDATE MODELS OF GENETIC REGULATORY NETWORKS

Machine learning methods to find graphical models of genetic regulatory networks from cDNA microarray data have become increasingly popular in recent years. We provide three reasons to question the reliability of such methods: (1) a major theoretical challenge to any method using conditional independence relations; (2) a simulation study using realistic data that confirms the importance of the theoretical challenge; and (3) an analysis of the computational complexity of algorithms that avoid this theoretical challenge. We have no proof that one cannot possibly learn the structure of a genetic regulatory network from microarray data alone, nor do we think that such a proof is likely. However, the combination of (i) fundamental challenges from theory, (ii) practical evidence that those challenges arise in realistic data, and (iii) the difficulty of avoiding those challenges leads us to conclude that it is unlikely that current microarray technology will ever be successfully applied to this structure learning problem.

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Hybrid-Controlled Neurofuzzy Networks Analysis Resulting in Genetic Regulatory Networks Reconstruction

ISRN Bioinformatics ◽

10.5402/2012/419419 ◽

2012 ◽

Vol 2012 ◽

pp. 1-16 ◽

Cited By ~ 2

Author(s):

Roozbeh Manshaei ◽

Pooya Sobhe Bidari ◽

Mahdi Aliyari Shoorehdeli ◽

Amir Feizi ◽

Tahmineh Lohrasebi ◽

...

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Regulatory Networks ◽

Expression Profiles ◽

Optimal Number ◽

Genetic Regulatory Networks ◽

Expression Data ◽

Time Series Gene Expression ◽

Neurofuzzy Networks ◽

Microarray Expression

Reverse engineering of gene regulatory networks (GRNs) is the process of estimating genetic interactions of a cellular system from gene expression data. In this paper, we propose a novel hybrid systematic algorithm based on neurofuzzy network for reconstructing GRNs from observational gene expression data when only a medium-small number of measurements are available. The approach uses fuzzy logic to transform gene expression values into qualitative descriptors that can be evaluated by using a set of defined rules. The algorithm uses neurofuzzy network to model genes effects on other genes followed by four stages of decision making to extract gene interactions. One of the main features of the proposed algorithm is that an optimal number of fuzzy rules can be easily and rapidly extracted without overparameterizing. Data analysis and simulation are conducted on microarray expression profiles of S. cerevisiae cell cycle and demonstrate that the proposed algorithm not only selects the patterns of the time series gene expression data accurately, but also provides models with better reconstruction accuracy when compared with four published algorithms: DBNs, VBEM, time delay ARACNE, and PF subjected to LASSO. The accuracy of the proposed approach is evaluated in terms of recall and F-score for the network reconstruction task.

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Clustering Genomic Expression Data: Design and Evaluation Principles

A Practical Approach to Microarray Data Analysis ◽

10.1007/0-306-47815-3_13 ◽

2005 ◽

pp. 230-245 ◽

Cited By ~ 2

Author(s):

Francisco Azuaje ◽

Nadia Bolshakova

Keyword(s):

Expression Data ◽

Genomic Expression ◽

Genomic Expression Data ◽

Data Design

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Retina Workbench: a flexible database system for manipulating and mining expression data and genetic regulatory networks

10.31274/rtd-180813-16204 ◽

2007 ◽

Author(s):

Oksana Kohutyuk

Keyword(s):

Regulatory Networks ◽

Database System ◽

Genetic Regulatory Networks ◽

Expression Data

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Abstract 386: Identification of Genetic Regulatory Networks for Insulin Resistance in Multiple Populations of Diverse Ethnicities

Circulation Research ◽

10.1161/res.121.suppl_1.386 ◽

2017 ◽

Vol 121 (suppl_1) ◽

Author(s):

Le Shu ◽

Yuqi Zhao ◽

Aldons J Lusis ◽

Ke Hao ◽

Thomas Quertermous ◽

...

Keyword(s):

Insulin Resistance ◽

Graphical Models ◽

Gene Networks ◽

Regulatory Networks ◽

Association Studies ◽

Network Models ◽

Mapk Signaling ◽

Genetic Regulatory Networks ◽

Specific Gene ◽

Genome Wide Association Studies

Insulin resistance (IR) is a critical pathogenic factor for highly prevalent modern cardiometabolic diseases, including coronary artery disease (CAD) and type 2 diabetes (T2D). However, the molecular circuitries underlying IR remain to be elucidated. The GENEticS of Insulin Sensitivity Consortium (GENESIS) conducted genome-wide association studies (GWAS) for direct measures of IR using euglycemic clamp or insulin suppression test. We sought to identify gene networks and their key intervening drivers for IR by performing a comprehensive integrative analysis leveraging GWAS data from seven GENESIS cohorts representing three ethnic groups - Europeans, Asians and Hispanics, along with expression quantitative trait loci, ENCODE, and tissue-specific gene network models (both co-expression and graphical models) from IR relevant tissues. Integration of the multi-ethnic GWAS with diverse functional genomics information captured shared IR pathways and networks across ethnicities that are independent of body mass index, including GLUT4 translocation regulation, insulin signaling, MAPK signaling, interleukin signaling, extracellular matrix, branched-chain amino acids metabolisms, cell cycle, and oxidative phosphorylation. Further integration of these GWAS-informed IR processes with graphical gene networks uncovered potential key regulators including HADH, COX5A, VCAN and TOP2A , whose network neighbors are consistently enriched for the genetic association signals of IR across ethnicities, and show significant correlation with IR, fasting glucose and insulin levels in the transcriptomic-wide association data from a Hybrid Mouse Diversity Panel comprised of >100 strains fed with high-fat diet. Findings from this in-depth assessment of genetic and functional data from multiple human cohorts provide new understanding of the pathways, gene networks and potential regulators contributing to IR. These results will also facilitate future functional investigations to unveil how DNA variations translate into IR.

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