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  • Poustka, Annemarie  (8)
  • 1
    Language: English
    In: BMC Bioinformatics, Oct 15, 2007, Vol.8(386), p.386
    Description: Background The advent of RNA interference techniques enables the selective silencing of biologically interesting genes in an efficient way. In combination with DNA microarray technology this enables researchers to gain insights into signaling pathways by observing downstream effects of individual knock-downs on gene expression. These secondary effects can be used to computationally reverse engineer features of the upstream signaling pathway. Results In this paper we address this challenging problem by extending previous work by Markowetz et al., who proposed a statistical framework to score networks hypotheses in a Bayesian manner. Our extensions go in three directions: First, we introduce a way to omit the data discretization step needed in the original framework via a calculation based on p-values instead. Second, we show how prior assumptions on the network structure can be incorporated into the scoring scheme using regularization techniques. Third and most important, we propose methods to scale up the original approach, which is limited to around 5 genes, to large scale networks. Conclusion Comparisons of these methods on artificial data are conducted. Our proposed module network is employed to infer the signaling network between 13 genes in the ER-[alpha] pathway in human MCF-7 breast cancer cells. Using a bootstrapping approach this reconstruction can be found with good statistical stability. The code for the module network inference method is available in the latest version of the R-package nem, which can be obtained from the Bioconductor homepage.
    Keywords: Cellular Signal Transduction -- Analysis ; Rna Interference -- Usage ; Dna Microarrays -- Usage ; Gene Expression -- Analysis
    ISSN: 1471-2105
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  • 2
    Language: English
    In: BMC bioinformatics, 15 October 2007, Vol.8, pp.386
    Description: The advent of RNA interference techniques enables the selective silencing of biologically interesting genes in an efficient way. In combination with DNA microarray technology this enables researchers to gain insights into signaling pathways by observing downstream effects of individual knock-downs on gene expression. These secondary effects can be used to computationally reverse engineer features of the upstream signaling pathway. In this paper we address this challenging problem by extending previous work by Markowetz et al., who proposed a statistical framework to score networks hypotheses in a Bayesian manner. Our extensions go in three directions: First, we introduce a way to omit the data discretization step needed in the original framework via a calculation based on p-values instead. Second, we show how prior assumptions on the network structure can be incorporated into the scoring scheme using regularization techniques. Third and most important, we propose methods to scale up the original approach, which is limited to around 5 genes, to large scale networks. Comparisons of these methods on artificial data are conducted. Our proposed module network is employed to infer the signaling network between 13 genes in the ER-alpha pathway in human MCF-7 breast cancer cells. Using a bootstrapping approach this reconstruction can be found with good statistical stability. The code for the module network inference method is available in the latest version of the R-package nem, which can be obtained from the Bioconductor homepage.
    Keywords: Gene Regulatory Networks -- Physiology ; Oligonucleotide Array Sequence Analysis -- Methods ; RNA Interference -- Physiology ; Signal Transduction -- Physiology
    E-ISSN: 1471-2105
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  • 3
    Language: English
    In: BMC Bioinformatics, 01 May 2007, Vol.8(1), p.166
    Description: Abstract Background With the increased availability of high throughput data, such as DNA microarray data, researchers are capable of producing large amounts of biological data. During the analysis of such data often there is the need to further explore the similarity of genes not only with...
    Keywords: Biology
    ISSN: 1471-2105
    E-ISSN: 1471-2105
    Source: Directory of Open Access Journals (DOAJ)
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  • 4
    Language: English
    In: BMC Bioinformatics, 01 October 2007, Vol.8(1), p.386
    Description: Abstract Background The advent of RNA interference techniques enables the selective silencing of biologically interesting genes in an efficient way. In combination with DNA microarray technology this enables researchers to gain insights into signaling pathways by observing downstream effects...
    Keywords: Biology
    ISSN: 1471-2105
    E-ISSN: 1471-2105
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 5
    Language: English
    In: BMC bioinformatics, 22 May 2007, Vol.8, pp.166
    Description: With the increased availability of high throughput data, such as DNA microarray data, researchers are capable of producing large amounts of biological data. During the analysis of such data often there is the need to further explore the similarity of genes not only with respect to their expression, but also with respect to their functional annotation which can be obtained from Gene Ontology (GO). We present the freely available software package GOSim, which allows to calculate the functional similarity of genes based on various information theoretic similarity concepts for GO terms. GOSim extends existing tools by providing additional lately developed functional similarity measures for genes. These can e.g. be used to cluster genes according to their biological function. Vice versa, they can also be used to evaluate the homogeneity of a given grouping of genes with respect to their GO annotation. GOSim hence provides the researcher with a flexible and powerful tool to combine knowledge stored in GO with experimental data. It can be seen as complementary to other tools that, for instance, search for significantly overrepresented GO terms within a given group of genes. GOSim is implemented as a package for the statistical computing environment R and is distributed under GPL within the CRAN project.
    Keywords: Data Interpretation, Statistical ; Databases, Genetic ; Programming Languages ; Software ; Sequence Analysis, DNA -- Methods
    E-ISSN: 1471-2105
    Source: MEDLINE/PubMed (U.S. National Library of Medicine)
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  • 6
    Language: English
    In: BMC Systems Biology, 01 January 2009, Vol.3(1), p.1
    Description: Abstract Background In breast cancer, overexpression of the transmembrane tyrosine kinase ERBB2 is an adverse prognostic marker, and occurs in almost 30% of the patients. For therapeutic intervention, ERBB2 is targeted by monoclonal antibody trastuzumab in adjuvant settings; however, de novo...
    Keywords: Biology
    ISSN: 1752-0509
    E-ISSN: 1752-0509
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  • 7
    In: Bioinformatics, 2008, Vol. 24(19), pp.2137-2142
    Description: Motivation: Functional characterization of genes is of great importance for the understanding of complex cellular processes. Valuable information for this purpose can be obtained from pathway databases, like KEGG. However, only a small fraction of genes is annotated with pathway information up to now. In contrast, information on contained protein domains can be obtained for a significantly higher number of genes, e.g. from the InterPro database. We present a classification model, which for a specific gene of interest can predict the mapping to a KEGG pathway, based on its domain signature. The classifier makes explicit use of the hierarchical organization of pathways in the KEGG database. Furthermore, we take into account that a specific gene can be mapped to different pathways at the same time. The classification method produces a scoring of all possible mapping positions of the gene in the KEGG hierarchy. Evaluations of our model, which is a combination of a SVM and ranking perceptron approach, show a high prediction performance. Moreover, for signaling pathways we reveal that it is even possible to forecast accurately the membership to individual pathway components. The R package is a supplement to this article. 〈p〉〈bold〉Contact:〈/bold〉 〈email〉h.froehlich@dkfz-heidelberg.de〈/email〉〈/p〉 are available at online.
    Keywords: Original Papers;
    ISSN: 1367-4803
    E-ISSN: 1460-2059
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  • 8
    In: Bioinformatics, 2008, Vol. 24(22), pp.2650-2656
    Description: Motivation: Targeted interventions using RNA interference in combination with the measurement of secondary effects with DNA microarrays can be used to computationally reverse engineer features of upstream non-transcriptional signaling cascades based on the nested structure of effects. We extend previous work by Markowetz , who proposed a statistical framework to score different network hypotheses. Our extensions go in several directions: we show how prior assumptions on the network structure can be incorporated into the scoring scheme by defining appropriate prior distributions on the network structure as well as on hyperparameters. An approach called is introduced to scale up the original approach, which is limited to around 5 genes, to infer large-scale networks of more than 30 genes. Instead of the data discretization step needed in the original framework, we propose the usage of a beta-uniform mixture distribution on the -value profile, resulting from differential gene expression calculation, to quantify effects. Extensive simulations on artificial data and application of our approach to infer the signaling network between 13 genes in the ER- pathway in human MCF-7 breast cancer cells show that our approach gives sensible results. Using a bootstrapping and a jackknife approach, this reconstruction is found to be statistically stable. The proposed method is available within the Bioconductor -package . 〈p〉〈bold〉Contact:〈/bold〉 〈email〉h.froehlich@dkfz-heidelberg.de〈/email〉〈/p〉
    Keywords: German Conference On Bioinformatics;
    ISSN: 1367-4803
    E-ISSN: 1460-2059
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