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65
Comparison of evolutionary algorithms in gene regulatory network model inference
- BMC Bioinform
"... Background: The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of di ..."
Abstract
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Cited by 9 (2 self)
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expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared. Conclusions: Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods
Improving Entropy Estimation and the Inference of Genetic Regulatory Networks
, 2006
"... This paper explores how entropy and other information theoretic quantities may be used to reverseengineer genetic regulatory networks from repeated microarray data. The problem of differentiating genes that undergo direct coregulation from genes whose expression is similar because they belong to the ..."
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Cited by 5 (0 self)
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This paper explores how entropy and other information theoretic quantities may be used to reverseengineer genetic regulatory networks from repeated microarray data. The problem of differentiating genes that undergo direct coregulation from genes whose expression is similar because they belong
Research Article Inferring Parameters of Gene Regulatory Networks via Particle Filtering
, 2010
"... License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Gene regulatory networks are highly complex dynamical systems comprising biomolecular components which interact with each other and through those interactions determin ..."
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License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Gene regulatory networks are highly complex dynamical systems comprising biomolecular components which interact with each other and through those interactions
Review Article State of the Art of Fuzzy Methods for Gene Regulatory Networks Inference
, 2014
"... Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. To address one of the most challenging issues at the cellular level, this paper surveys the fuzzy methods used in gene regulatory networks (GRNs) inference ..."
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Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. To address one of the most challenging issues at the cellular level, this paper surveys the fuzzy methods used in gene regulatory networks (GRNs
Inferring Regulatory Networks from Expression Data Using Tree-Based Methods
"... One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs) using high throughput genomic data, in particular microarray gene expression data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) challeng ..."
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One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs) using high throughput genomic data, in particular microarray gene expression data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM
METHODOLOGY ARTICLE Open Access Fast Bayesian inference for gene regulatory
"... Yeung2* Background: Genome-wide time-series data provide a rich set of information for discovering gene regulatory relationships. As genome-wide data for mammalian systems are being generated, it is critical to develop network inference methods that can handle tens of thousands of genes efficiently, ..."
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Yeung2* Background: Genome-wide time-series data provide a rich set of information for discovering gene regulatory relationships. As genome-wide data for mammalian systems are being generated, it is critical to develop network inference methods that can handle tens of thousands of genes efficiently
Inferring Nonlinear Gene Regulatory Networks from Gene Expression Data Based on Distance Correlation
, 2014
"... Nonlinear dependence is general in regulation mechanism of gene regulatory networks (GRNs). It is vital to properly measure or test nonlinear dependence from real data for reconstructing GRNs and understanding the complex regulatory mechanisms within the cellular system. A recently developed measure ..."
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Cited by 3 (0 self)
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measurement called the distance correlation (DC) has been shown powerful and computationally effective in nonlinear dependence for many situations. In this work, we incorporate the DC into inferring GRNs from the gene expression data without any underling distribution assumptions. We propose three DC
Systems biology Combining tree-based and dynamical systems for the inference of gene regulatory networks
"... Motivation: Reconstructing the topology of gene regulatory networks (GRNs) from time series of gene expression data remains an important open problem in computational systems biology. Existing GRN inference algorithms face one of two limitations: model-free methods are scalable but suffer from a lac ..."
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Motivation: Reconstructing the topology of gene regulatory networks (GRNs) from time series of gene expression data remains an important open problem in computational systems biology. Existing GRN inference algorithms face one of two limitations: model-free methods are scalable but suffer from a
RESEARCH ARTICLE Time Delayed Causal Gene Regulatory Network Inference with Hidden Common Causes
"... Inferring the gene regulatory network (GRN) is crucial to understanding the working of the cell. Many computational methods attempt to infer the GRN from time series expression data, instead of through expensive and time-consuming experiments. However, existing methods make the convenient but unreal ..."
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Inferring the gene regulatory network (GRN) is crucial to understanding the working of the cell. Many computational methods attempt to infer the GRN from time series expression data, instead of through expensive and time-consuming experiments. However, existing methods make the convenient
Research Article Gene Regulatory Network Reconstruction Using Conditional Mutual Information
"... The inference of gene regulatory network from expression data is an important area of research that provides insight to the inner workings of a biological system. The relevance-network-based approaches provide a simple and easily-scalable solution to the understanding of interaction between genes. U ..."
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The inference of gene regulatory network from expression data is an important area of research that provides insight to the inner workings of a biological system. The relevance-network-based approaches provide a simple and easily-scalable solution to the understanding of interaction between genes
Results 1 - 10
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65
