How to infer gene networks from expression profiles

Bansal, Mukesh; Belcastro, Vincenzo; Ambesi-Impiombato, Alberto; Bernardo, Diego di

doi:10.1038/msb4100120

Cited by 623 publications

(513 citation statements)

References 28 publications

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“…These results are contrary to the opinion of Chickering (1996) that greedy search outperforms simulated annealing in inferring BN structures. However, it affirms the assertion that there are no clear-cut superior approaches among the existing algorithms as each performs better under different conditions of data (Bansal, Belcastro, Ambesi-Impiobato, & Bernardo, 2007). In all situations, the results of…”

Section: Discussionmentioning

confidence: 51%

“…The BN models direct causal relationships as a direct acyclic graph (DAG) when the Causal Markov Assumption holds. This assumption can be stated as: a variable X is independent of every other variable conditional on all its causes (Bansal, Belcastro, Ambesi-Impiobato, & Bernardo, 2007). This work further seeks to establish optimal settings for building a Bayesian Network using actual breast cancer data.…”

Section: Introductionmentioning

confidence: 99%

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Inferring Transcriptional Regulatory Relationships Among Genes in Breast Cancer: An Application of Bayes' Theorem

Adabor¹,

Acquaah‐Mensah²,

Oduro³

2014

IJSP

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The introduction of Deoxyribonucleic acid (DNA) microarray technologies provides a means of measuring the expression of thousands of genes simultaneously. It has generally sought to revolutionalize biological research by significantly elucidating biological processes. Gene networks may be inferred from such microarray data. Bayes' theorem, in this work is applied to the problem of inferring new transcriptional regulatory relationships among gene products in Breast Cancer. A compendium of human breast epithelial cell probe level microarray data from the Gene Expression Omnibus (GEO) repository was subjected to the Robust Multiarray Average (RMA) procedure for normalization and background correction. A subset of the resulting expression matrix consisting of the expression values of only relevant probe-set identifiers (IDs) representing the genes of interest in the data were extracted with a LISP code. This subset was supplied to a Bayesian Network inference learning algorithm to unearth new regulatory relationships from the data. Variations in parameters of the learning algorithms resulted in the prediction of at least 10 new relationships among genes in breast cancer. Among these were the direct regulatory signaling relationship between S-phase kinase associated protein 2 (SKP2) and the Cell division cycle 25A (CDC25A) and that between the cyclin-dependent kinases regulatory subunit 1 (CKS1B / CDC28) and E2F transcription factor 3 (E2F3). The identified causal networks are potentially useful for understanding complex drug actions and dysfunctional signaling in breast cancer.

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Section: Discussionmentioning

confidence: 51%

Section: Introductionmentioning

confidence: 99%

Inferring Transcriptional Regulatory Relationships Among Genes in Breast Cancer: An Application of Bayes' Theorem

Adabor¹,

Acquaah‐Mensah²,

Oduro³

2014

IJSP

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“…In the last few years, a number of methods for network inference have been developed (for a recent review on the subject see Bansal et al 2007). These include relevance networks (RNs), information-theoretic approaches such as ARACNE (algorithm for the reconstruction of accurate cellular networks), BNs and differential equations-based methods.…”

Section: Inferring the Connectivity Of Cellular Network (A ) Methodomentioning

confidence: 99%

“…Different inference methodologies have been compared systematically by Werhli et al (2006) and Bansal et al (2007). Werhli et al (2006) have used the Raf pathway as a model system with both simulated and experimental data.…”

Section: (B ) Validation Of Network Inference Methodologiesmentioning

confidence: 99%

“…They found that BNs produce significantly better results than RNs when edge directions are taken into account. More recently, Bansal et al (2007) used a number of synthetic networks simulating time course with local (a single gene is perturbed) and global (all genes are perturbed simultaneously in the network) perturbation experiments to compare the accuracy of ARACNE, BNs, clustering and NIR. Overall, the results of these validation studies suggest that both BNs and RNs have a very strong potential in reverse engineering biological circuits for which a sufficiently large number of experimental samples is available.…”

Section: (B ) Validation Of Network Inference Methodologiesmentioning

confidence: 99%

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Models and computational strategies linking physiological response to molecular networks from large-scale data

Ortega

Sameith

Turan

et al. 2008

Phil. Trans. R. Soc. A.

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An important area of research in systems biology involves the analysis and integration of genome-wide functional datasets. In this context, a major goal is the identification of a putative molecular network controlling physiological response from experimental data. With very fragmentary mechanistic information, this is a challenging task. A number of methods have been developed, each one with the potential to address an aspect of the problem. Here, we review some of the most widely used methodologies and report new results in support of the usefulness of modularization and other modelling techniques in identifying components of the molecular networks that are predictive of physiological response. We also discuss how system identification in biology could be approached, using a combination of methodologies that aim to reconstruct the relationship between molecular pathways and physiology at different levels of the organizational complexity of the molecular network.

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Reconstruction of Gene Regulatory Networks

Chen

Wang

Zhang³

2009

Biomolecular Networks

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pagesWith the development of microarray technology, it is now possible to obtain the concentration levels of thousands of genes at a given time or in a given state. By following the changes in the gene expression levels, the responsible genes for cell differentiation or certain diseases can be identified. Gene expression changes are regulated by the interactions between the genes and their products. Gene regulatory networks (GRNs) identify these interactions using the gene expression changes. There are a number of statistical methods to infer GRNs, however, most of them depend on the normality assumption of noises in the data. This thesis considers the multiple linear regression analysis for the reconstruction of GRNs when the error term comes from a Weibull distribution. Since nonnormality complicates the data analysis and results in inefficient estimators, it is proposed to use the modified maximum likelihood (MML) estimation procedure which produces efficient and robust estimators. Also, explanatory variables representing the gene expression levels come from a Weibull distribution. Therefore, they are considered as stochastic and stochastic multiple linear regression analysis is used for inferring GRNs by implementing MML method to estimate the model vi parameters. Robustness and power analyses for both stochastic and nonstochastic multiple linear regression model parameters are also given.

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How to infer gene networks from expression profiles

Cited by 623 publications

References 28 publications

Inferring Transcriptional Regulatory Relationships Among Genes in Breast Cancer: An Application of Bayes' Theorem

Inferring Transcriptional Regulatory Relationships Among Genes in Breast Cancer: An Application of Bayes' Theorem

Models and computational strategies linking physiological response to molecular networks from large-scale data

Reconstruction of Gene Regulatory Networks

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