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      Wisdom of crowds for robust gene network inference

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          Abstract

          Reconstructing gene regulatory networks from high-throughput data is a long-standing problem. Through the DREAM project (Dialogue on Reverse Engineering Assessment and Methods), we performed a comprehensive blind assessment of over thirty network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae, and in silico microarray data. We characterize performance, data requirements, and inherent biases of different inference approaches offering guidelines for both algorithm application and development. We observe that no single inference method performs optimally across all datasets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse datasets. Thereby, we construct high-confidence networks for E. coli and S. aureus, each comprising ~1700 transcriptional interactions at an estimated precision of 50%. We experimentally test 53 novel interactions in E. coli, of which 23 were supported (43%). Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks.

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          Most cited references 65

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          A comparison of normalization methods for high density oligonucleotide array data based on variance and bias.

          When running experiments that involve multiple high density oligonucleotide arrays, it is important to remove sources of variation between arrays of non-biological origin. Normalization is a process for reducing this variation. It is common to see non-linear relations between arrays and the standard normalization provided by Affymetrix does not perform well in these situations. We present three methods of performing normalization at the probe intensity level. These methods are called complete data methods because they make use of data from all arrays in an experiment to form the normalizing relation. These algorithms are compared to two methods that make use of a baseline array: a one number scaling based algorithm and a method that uses a non-linear normalizing relation by comparing the variability and bias of an expression measure. Two publicly available datasets are used to carry out the comparisons. The simplest and quickest complete data method is found to perform favorably. Software implementing all three of the complete data normalization methods is available as part of the R package Affy, which is a part of the Bioconductor project http://www.bioconductor.org. Additional figures may be found at http://www.stat.berkeley.edu/~bolstad/normalize/index.html
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            Regression Shrinkage and Selection Via the Lasso

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              Modularity and community structure in networks

               M. Newman (2006)
              Many networks of interest in the sciences, including a variety of social and biological networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure has attracted considerable recent attention. One of the most sensitive detection methods is optimization of the quality function known as "modularity" over the possible divisions of a network, but direct application of this method using, for instance, simulated annealing is computationally costly. Here we show that the modularity can be reformulated in terms of the eigenvectors of a new characteristic matrix for the network, which we call the modularity matrix, and that this reformulation leads to a spectral algorithm for community detection that returns results of better quality than competing methods in noticeably shorter running times. We demonstrate the algorithm with applications to several network data sets.
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                Author and article information

                Journal
                101215604
                32338
                Nat Methods
                Nat. Methods
                Nature methods
                1548-7091
                1548-7105
                17 November 2012
                15 July 2012
                01 February 2013
                : 9
                : 8
                : 796-804
                Affiliations
                [1 ]Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, USA
                [2 ]Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
                [3 ]Howard Hughes Medical Institute, Department of Biomedical Engineering, and Center for BioDynamics, Boston University, Boston, Massachusetts, USA
                [4 ]Ludwig-Maximilians University, Department of Informatics, Munich, Germany
                [5 ]IBM T. J. Watson Research Center, Yorktown Heights, New York, USA
                Author notes
                [7 ]Correspondence should be addressed to G.S. ( gustavo@ 123456us.ibm.com )
                [6]

                The complete list of contributors appears at the end of the paper.

                [8]

                These authors contributed equally to this work.

                Article
                NIHMS420148
                10.1038/nmeth.2016
                3512113
                22796662

                Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

                Funding
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: R01 HG004037 || HG
                Categories
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                Life sciences

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