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  • Oxford University Press (OUP)  (10)
  • 1
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2008
    In:  Bioinformatics Vol. 24, No. 22 ( 2008-11-15), p. 2650-2656
    In: Bioinformatics, Oxford University Press (OUP), Vol. 24, No. 22 ( 2008-11-15), p. 2650-2656
    Abstract: 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. Results: We extend previous work by Markowetz et al., 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 module networks 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 P-value profile, resulting from differential gene expression calculation, to quantify effects. Extensive simulations on artificial data and application of our module network approach to infer the signaling network between 13 genes in the ER-α 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. Availability: The proposed method is available within the Bioconductor R-package nem. Contact:  h.froehlich@dkfz-heidelberg.de
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2008
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 2
    In: Bioinformatics, Oxford University Press (OUP), Vol. 26, No. 17 ( 2010-09-01), p. 2136-2144
    Abstract: Motivation: One of the main goals of high-throughput gene-expression studies in cancer research is to identify prognostic gene signatures, which have the potential to predict the clinical outcome. It is common practice to investigate these questions using classification methods. However, standard methods merely rely on gene-expression data and assume the genes to be independent. Including pathway knowledge a priori into the classification process has recently been indicated as a promising way to increase classification accuracy as well as the interpretability and reproducibility of prognostic gene signatures. Results: We propose a new method called Reweighted Recursive Feature Elimination. It is based on the hypothesis that a gene with a low fold-change should have an increased influence on the classifier if it is connected to differentially expressed genes. We used a modified version of Google's PageRank algorithm to alter the ranking criterion of the SVM-RFE algorithm. Evaluations of our method on an integrated breast cancer dataset comprising 788 samples showed an improvement of the area under the receiver operator characteristic curve as well as in the reproducibility and interpretability of selected genes. Availability: The R code of the proposed algorithm is given in Supplementary Material. Contact:  m.johannes@DKFZ-heidelberg.de; tim.beissbarth@ams.med.uni-goettingen.de Supplementary information:  Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2010
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 3
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2011
    In:  Bioinformatics Vol. 27, No. 10 ( 2011-05-15), p. 1442-1443
    In: Bioinformatics, Oxford University Press (OUP), Vol. 27, No. 10 ( 2011-05-15), p. 1442-1443
    Abstract: Summary: Prognostic and diagnostic biomarker discovery is one of the key issues for a successful stratification of patients according to clinical risk factors. For this purpose, statistical classification methods, such as support vector machines (SVM), are frequently used tools. Different groups have recently shown that the usage of prior biological knowledge significantly improves the classification results in terms of accuracy as well as reproducibility and interpretability of gene lists. Here, we introduce pathClass, a collection of different SVM-based classification methods for improved gene selection and classfication performance. The methods contained in pathClass do not merely rely on gene expression data but also exploit the information that is carried in gene network data. Availability:  pathClass is open source and freely available as an R-Package on the CRAN repository at http://cran.r-project.org Contact:  m.johannes@dkfz-heidelberg.de; tim.beissbarth@ams.med.uni-goettingen.de
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2011
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 4
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2008
    In:  Bioinformatics Vol. 24, No. 19 ( 2008-10-01), p. 2137-2142
    In: Bioinformatics, Oxford University Press (OUP), Vol. 24, No. 19 ( 2008-10-01), p. 2137-2142
    Abstract: 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. Results: 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. Availability: The R package gene2pathway is a supplement to this article. Contact:  h.froehlich@dkfz-heidelberg.de Supplementary Information:  Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2008
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 5
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 45, No. 6 ( 2017-04-07), p. e44-e44
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    RVK:
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2017
    detail.hit.zdb_id: 1472175-2
    SSG: 12
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  • 6
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2002
    In:  Bioinformatics Vol. 18, No. suppl_1 ( 2002-07-01), p. S96-S104
    In: Bioinformatics, Oxford University Press (OUP), Vol. 18, No. suppl_1 ( 2002-07-01), p. S96-S104
    Abstract: We introduce a statistical model for microarray gene expression data that comprises data calibration, the quantification of differential expression, and the quantification of measurement error. In particular, we derive a transformation h for intensity measurements, and a difference statistic Δh whose variance is approximately constant along the whole intensity range. This forms a basis for statistical inference from microarray data, and provides a rational data pre-processing strategy for multivariate analyses. For the transformation h, the parametric form h(x)=arsinh(a+bx) is derived from a model of the variance-versus-mean dependence for microarray intensity data, using the method of variance stabilizing transformations. For large intensities, h coincides with the logarithmic transformation, and Δh with the log-ratio. The parameters of h together with those of the calibration between experiments are estimated with a robust variant of maximum-likelihood estimation. We demonstrate our approach on data sets from different experimental platforms, including two-colour cDNA arrays and a series of Affymetrix oligonucleotide arrays. Availability: Software is freely available for academic use as an R package at http://www.dkfz.de/abt0840/whuber Contact: w.huber@dkfz.de
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2002
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 7
    In: Bioinformatics, Oxford University Press (OUP), Vol. 23, No. 17 ( 2007-09-01), p. 2273-2280
    Abstract: Motivation: In cancer, chromosomal imbalances like amplifications and deletions, or changes in epigenetic mechanisms like DNA methylation influence the transcriptional activity. These alterations are often not limited to a single gene but affect several genes of the genomic region and may be relevant for the disease status. For example, the ERBB2 amplicon (17q21) in breast cancer is associated with poor patient prognosis. We present a general, unsupervised method for genome-wide gene expression data to systematically detect tumor patients with chromosomal regions of distinct transcriptional activity. The method aims to find expression patterns of adjacent genes with a consistently decreased or increased level of gene expression in tumor samples. Such patterns have been found to be associated with chromosomal aberrations and clinical parameters like tumor grading and thus can be useful for risk stratification or therapy. Results: Our approach was applied to 12 independent human breast cancer microarray studies comprising 1422 tumor samples. We prioritized chromosomal regions and genes predominantly found across all studies. The result highlighted not only regions which are well known to be amplified like 17q21 and 11q13, but also others like 8q24 (distal to MYC) and 17q24-q25 which may harbor novel putative oncogenes. Since our approach can be applied to any microarray study it may become a valuable tool for the exploration of transcriptional changes in diverse disease types. Availability: The R source codes which implement the method and an exemplary analysis are available at http://www.dkfz.de/mga2/people/buness/CTP/. Contact:  a.buness@gmx.de Supplementary information: Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2007
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 8
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2005
    In:  Bioinformatics Vol. 21, No. 4 ( 2005-02-15), p. 554-556
    In: Bioinformatics, Oxford University Press (OUP), Vol. 21, No. 4 ( 2005-02-15), p. 554-556
    Abstract: Summary: arrayMagic is a software package for quality control and preprocessing of two-colour cDNA microarray data. The automated analysis pipeline comprises data import, normalization, replica merging, quality diagnostics and data export. The script-based processing combines reproducibility and flexibility at high-throughput and provides quality-assured and preprocessed microarray data to high-level follow-up analysis. Availability: The R package arrayMagic is available with BSD license at http://www.bioconductor.org Contact:  a.buness@dkfz.de Supplementary information: The package contains documentation in the form of manual pages and a vignette with a guided tour of a typical workflow.
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2005
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 9
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2018
    In:  Briefings in Bioinformatics Vol. 19, No. 5 ( 2018-09-28), p. 918-929
    In: Briefings in Bioinformatics, Oxford University Press (OUP), Vol. 19, No. 5 ( 2018-09-28), p. 918-929
    Type of Medium: Online Resource
    ISSN: 1467-5463 , 1477-4054
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2018
    detail.hit.zdb_id: 2036055-1
    SSG: 12
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  • 10
    In: Genetics, Oxford University Press (OUP), Vol. 149, No. 3 ( 1998-07-01), p. 1527-1537
    Abstract: The species flocks of cichlid fishes in the Great East African Lakes are paradigms of adaptive radiation and hence, of great interest to evolutionary biologists. Phylogenetic studies of these fishes have, however, been hampered by the lack of suitable polymorphic markers. The genes of the major histocompatibility complex hold the promise to provide, through their extensive polymorphism, a large number of such markers, but their use has been hampered by the complexity of the genetic system and the lack of definition of the individual loci. In this study we take the first substantial step to alleviate this problem. Using a combination of methods, including the typing of single sperm cells, gyno- or androgenetic individuals, and haploid embryos, as well as sequencing of class II B restriction fragments isolated from gels for Southern blots, we identify the previously characterized homology groups as distinct loci. At least 17 polymorphic class II B loci, all of which are presumably transcribed, have been found among the different species studied. Most of these loci are shared across the various cichlid species and genera. The number of loci per haplotype varies from individual to individual, ranging from 1 to 13. A total of 21 distinct haplotypes differing in the number of loci they carry has thus far been identified. All the polymorphic loci are part of the same cluster in which, however, distances between at least some of the loci (as indicated by recombination frequencies) are relatively large. Both the individual loci and the haplotypes can now be used to study phylogenetic relationships among the members of the species flocks and the mode in which speciation occurs during adaptive radiation.
    Type of Medium: Online Resource
    ISSN: 1943-2631
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 1998
    detail.hit.zdb_id: 1477228-0
    SSG: 12
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