Inferring genetic networks from microarray data (original) (raw)
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GeneNetwork: an interactive tool for reconstruction of genetic networks using microarray data
Bioinformatics/computer Applications in The Biosciences, 2004
Inferring genetic network architecture from time series data generated from high-throughput experimental technologies, such as cDNA microarray, can help us to understand the system behavior of living organisms. We have developed an interactive tool, GeneNetwork, which provides four reverse engineering models and three data interpolation approaches to infer relationships between genes. GeneNetwork enables a user to readily reconstruct genetic networks based on microarray data without having intimate knowledge of the mathematical models. A simple graphical user interface enables rapid, intuitive mapping and analysis of the reconstructed network allowing biologists to explore gene relationships at the system level. Availability: Download from http://
Inferring Gene Networks from Microarray Data by Closed-loop Optimization
2009
In this article we propose an inference algorithm for constructing gene networks from time series data of microarray experiments that optimizes the closed-loop formed by the data and the algorithm itself. Moreover, the suggested inference algorithm is based on partial coherences which is also novel in the this context.
A deterministic model to infer gene networks from microarray data
2007
Microarray experiments help researches to construct the structure of gene regulatory networks, i.e., networks representing relationships among dierent genes. Filter and knowledge extraction processes are necessary in order to handle the huge amount of data produced by microarray technologies. We propose regression trees techniques as a method to identify gene networks. Regression trees are a very useful technique to estimate the numerical values for the target outputs. They are very often more precise than linear regression models because they can adjust dierent linear regressions to separate areas of the search space. In our approach, we generate a single regression tree for each genes from a set of genes, taking as input the remaining genes, to nally build a graph from all the relationships among output and input genes. In this paper, we will simplify the approach by setting an only seed, the gene ARN1, and building the graph around it. The nal model might gives some clues to understand the dynamics, the regulation or the topology of the gene network from one (or several) seeds, since it gathers relevant genes with accurate connections. The performance of our approach is experimentally tested on the yeast Saccharomyces cerevisiae dataset (Rosetta compendium).
Inferring Gene Regulatory Networks from Asynchronous Microarray Data
Biocomp, 2009
Modern approaches to treating genetic disorders, cancers and even epidemics rely on a detailed understanding of the underlying gene signaling network. Previous work has used time series microarray data to infer gene signaling networks given a large number of accurate time series samples. Microarray data available for many biological experiments is limited to a small number of arrays with little or no time series guarantees. Asynchronous Inference of Regulatory Networks (AIRnet) provides gene signaling network inferrence using more practical assumptions about the microarray data. By learning correlation patterns from all pairs of microarray samples, accurate network reconstructions can be performed with data that is normally available in microarray experiments.
Inferring Gene Networks: Dream or Nightmare?
Annals of the New York Academy of Sciences, 2009
We describe several algorithms with winning performance in the Dialogue for Reverse Engineering Assessments and Methods (DREAM2) Reverse Engineering Competition 2007. After the gold standards for the challenges were released, the performance of the algorithms could be thoroughly evaluated under different parameters or alternative ways of solving systems of equations. For the analysis of Challenge 4, the "In-silico" challenges, we employed methods to explicitly deal with perturbation data and timeseries data. We show that original methods used to produce winning submissions could easily be altered to substantially improve performance. For Challenge 5, the genomescale Escherichia coli network, we evaluated a variety of measures of association. These data are troublesome, and no good solutions could be produced, either by us or by any other teams. Our best results were obtained when analyzing subdatasets instead of considering the dataset as a whole.
Consistency of biological networks inferred from microarray and sequencing data
BMC bioinformatics, 2016
Sparse Gaussian graphical models are popular for inferring biological networks, such as gene regulatory networks. In this paper, we investigate the consistency of these models across different data platforms, such as microarray and next generation sequencing, on the basis of a rich dataset containing samples that are profiled under both techniques as well as a large set of independent samples. Our analysis shows that individual node variances can have a remarkable effect on the connectivity of the resulting network. Their inconsistency across platforms and the fact that the variability level of a node may not be linked to its regulatory role mean that, failing to scale the data prior to the network analysis, leads to networks that are not reproducible across different platforms and that may be misleading. Moreover, we show how the reproducibility of networks across different platforms is significantly higher if networks are summarised in terms of enrichment amongst functional groups...
PLoS ONE, 2010
Background: Current technologies have lead to the availability of multiple genomic data types in sufficient quantity and quality to serve as a basis for automatic global network inference. Accordingly, there are currently a large variety of network inference methods that learn regulatory networks to varying degrees of detail. These methods have different strengths and weaknesses and thus can be complementary. However, combining different methods in a mutually reinforcing manner remains a challenge.
predictionet: a package for inferring predictive networks from high-dimensional genomic data
2012
DNA microarrays and other high-throughput omics technologies provide large datasets that often include patterns of correlation between genes reflecting the complex processes that underlie cellular processes. The challenge in analyzing large-scale expression data has been to extract biologically meaningful inferences regarding these processes–often represented as networks–in an environment where the datasets are complex and noisy.
How to infer gene networks from expression profiles
Molecular Systems Biology, 2007
Inferring, or 'reverse-engineering', gene networks can be defined as the process of identifying gene interactions from experimental data through computational analysis. Gene expression data from microarrays are typically used for this purpose. Here we compared different reverseengineering algorithms for which ready-to-use software was available and that had been tested on experimental data sets. We show that reverse-engineering algorithms are indeed able to correctly infer regulatory interactions among genes, at least when one performs perturbation experiments complying with the algorithm requirements. These algorithms are superior to classic clustering algorithms for the purpose of finding regulatory interactions among genes, and, although further improvements are needed, have reached a discreet performance for being practically useful.
Nucleic Acids Research, 2011
Gene regulatory networks inferred from RNA abundance data have generated significant interest, but despite this, gene network approaches are used infrequently and often require input from bioinformaticians. We have assembled a suite of tools for analysing regulatory networks, and we illustrate their use with microarray datasets generated in human endothelial cells. We infer a range of regulatory networks, and based on this analysis discuss the strengths and limitations of network inference from RNA abundance data. We welcome contact from researchers interested in using our inference and visualization tools to answer biological questions.