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  • NETWORKS  (7)
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  • 1
    Keywords: - ; comparison ; UPSTREAM ; microbiology ; ENGLAND ; technique ; computational biology ; methods ; DNA-MICROARRAY ; GO ; INTERFERENCE ; SCALE ; RNA INTERFERENCE ; EXTENSION ; RE ; FEATURES ; signaling ; German ; in combination ; FALSE DISCOVERY RATE ; SIGNALING NETWORK ; signaling networks ; biotechnology ; RNA ; microarray ; GENES ; DNA ; GENE ; GENE-EXPRESSION ; NETWORK ; NETWORKS ; CELL ; Germany ; CELLS ; EXPRESSION ; CANCER CELLS ; CANCER ; PATHWAYS ; MODEL ; MODELS ; PATHWAY ; human ; COMBINATION ; STABILITY ; bioinformatics ; CANCER-CELLS ; SIGNALING PATHWAYS ; SIGNALING PATHWAY ; EFFICIENT ; RECONSTRUCTION ; BIOLOGY ; INTERVENTION ; BREAST ; breast cancer ; BREAST-CANCER ; gene expression ; MICROARRAY DATA
    Type of Publication: Book chapter
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  • 2
    Keywords: computational biology ; NETWORK ; NETWORKS ; GENE ; bioinformatics ; BIOLOGY
    Type of Publication: Book chapter
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  • 3
    Keywords: INHIBITOR ; Germany ; human ; IN-VIVO ; MODEL ; VIVO ; CLASSIFICATION ; NETWORK ; NETWORKS ; neural networks ; SUPPORT ; SYSTEM ; MICE ; TIME ; RAT ; animals ; RATS ; VECTOR ; BEHAVIOR ; support vector machines ; RE ; development ; PROFILES ; technique ; function ; EVALUATE ; COMPOUND ; in vivo ; animal ; ENGLAND ; SET ; PROFILE ; German ; ANTIDEPRESSANT ACTIVITY ; automated behavior classification ; forced swimming test ; kernel ; NEURAL-NETWORKS ; support vector machine
    Abstract: The forced swimming test of rats or mice is a frequently used behavioral test to evaluate compounds for antidepressant activity in vivo. The aim of this study was to replace the human observer, needed to score and analyze the behavior of animals, by a fully automated method. For this purpose, in a first step from a video recording of each rat, an activity profile was calculated, from which subsequently a set of meaningful properties was extracted. This set was finally used to train a Support Vector Machine (SVM). Furthermore, specialized kernel functions, namely the so-called time resolved p-spectrum and modified optimal assignment kernels, were developed to calculate the similarity of activity profiles. Our method allows for a very reliable discrimination of animals treated with antidepressants of different classes (tricyclics imipramine and desipramine as well as selective serotonin reuptake inhibitor, SSRI, fluoxetine) versus a vehicle-treated group. Moreover, our technique is able to classify between tricyclic antidepressants and SSRIs. The results of this work enabled the development of an automated and highly accurate behavior classification system. (c) 2007 Elsevier Ltd. All rights reserved
    Type of Publication: Journal article published
    PubMed ID: 18158234
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  • 4
    Keywords: CANCER ; CANCER CELLS ; CELLS ; EXPRESSION ; CELL ; COMBINATION ; Germany ; human ; PATHWAY ; PATHWAYS ; NETWORK ; NETWORKS ; GENE ; GENE-EXPRESSION ; GENES ; microarray ; RNA ; DNA ; BIOLOGY ; BREAST ; breast cancer ; BREAST-CANCER ; gene expression ; MICROARRAY DATA ; EFFICIENT ; SIGNALING PATHWAY ; SIGNALING PATHWAYS ; CANCER-CELLS ; bioinformatics ; STABILITY ; RECONSTRUCTION ; signaling ; FEATURES ; RE ; INTERFERENCE ; SCALE ; RNA INTERFERENCE ; EXTENSION ; GO ; methods ; DNA-MICROARRAY ; TECHNOLOGY ; computational biology ; technique ; microbiology ; ENGLAND ; UPSTREAM ; comparison ; in combination ; biotechnology ; German ; FALSE DISCOVERY RATE ; SIGNALING NETWORK
    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 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
    Type of Publication: Journal article published
    PubMed ID: 17937790
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  • 5
    Keywords: CANCER ; CANCER CELLS ; CELLS ; EXPRESSION ; CELL ; COMBINATION ; Germany ; human ; MODEL ; MODELS ; PATHWAY ; NETWORKS ; GENE ; GENE-EXPRESSION ; GENES ; microarray ; RNA ; DNA ; SIMULATION ; BIOLOGY ; BREAST ; breast cancer ; BREAST-CANCER ; score ; gene expression ; MICROARRAY DATA ; DNA microarray ; microarrays ; CANCER-CELLS ; bioinformatics ; RECONSTRUCTION ; signaling ; FEATURES ; RE ; INTERFERENCE ; RNA INTERFERENCE ; EXTENSION ; methods ; microbiology ; ENGLAND ; NOV ; UPSTREAM ; PROFILE ; biotechnology ; SIGNALING NETWORK ; BREAST-CANCER-CELLS ; INTERVENTIONS ; PROBABILITY ; differential gene expression
    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-alpha 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
    Type of Publication: Journal article published
    PubMed ID: 18227117
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  • 6
    Keywords: Germany ; MODEL ; MODELS ; PATHWAY ; NETWORK ; NETWORKS ; SIMULATION ; YEAST ; FRAMEWORK ; structure ; methods ; signalling ; FORMULATION ; INTERVENTIONS ; STATE ; Nested effects models
    Abstract: Nested effects models (NEMs) are a class of probabilistic models that were designed to reconstruct a hidden signalling structure from a large set of observable effects caused by active interventions into the signalling pathway. We give a more flexible formulation of NEMs in the language of Bayesian networks. Our framework constitutes a natural generalization of the original NEM model, since it explicitly states the assumptions that are tacitly underlying the original version. Our approach gives rise to new learning methods for NEMs, which have been implemented in the R/Bioconductor package nem. We validate these methods in a simulation study and apply them to a synthetic lethality dataset in yeast.
    Type of Publication: Journal article published
    PubMed ID: 19148294
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  • 7
    Keywords: MODEL ; NETWORKS ; PROTEIN ; ESCHERICHIA-COLI ; TARGETS ; OVARIAN-CANCER CELLS ; INTEGRATED ANALYSIS ; REGULATORY SYSTEM ; ANAEROBIC GROWTH ; WEB TOOL
    Abstract: MOTIVATION: There have been many successful experimental and bioinformatics efforts to elucidate transcription factor (TF)-target networks in several organisms. For many organisms, these annotations are complemented by miRNA-target networks of good quality. Attempts that use these networks in combination with gene expression data to draw conclusions on TF or miRNA activity are, however, still relatively sparse. RESULTS: In this study, we propose Bayesian inference of regulation of transcriptional activity (BIRTA) as a novel approach to infer both, TF and miRNA activities, from combined miRNA and mRNA expression data in a condition specific way. That means our model explains mRNA and miRNA expression for a specific experimental condition by the activities of certain miRNAs and TFs, hence allowing for differentiating between switches from active to inactive (negative switch) and inactive to active (positive switch) forms. Extensive simulations of our model reveal its good prediction performance in comparison to other approaches. Furthermore, the utility of BIRTA is demonstrated at the example of Escherichia coli data comparing aerobic and anaerobic growth conditions, and by human expression data from pancreas and ovarian cancer. Availability and implementation: The method is implemented in the R package birta, which is freely available for Bio-conductor (〉=2.10) on http://www.bioconductor.org/packages/release/bioc/html/birta.html. CONTACT: frohlich@bit.uni-bonn.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
    Type of Publication: Journal article published
    PubMed ID: 22563068
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