Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • Germany  (12)
  • ENGLAND  (6)
Collection
Keywords
  • 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
    Signatur Availability
    BibTip Others were also interested in ...
  • 2
    Keywords: EXPRESSION ; COMBINATION ; evaluation ; Germany ; MODEL ; PATHWAY ; PATHWAYS ; CLASSIFICATION ; INFORMATION ; GENE ; GENES ; PROTEIN ; COMPONENTS ; TIME ; COMPLEX ; DOMAIN ; CONTRAST ; BIOLOGY ; PERFORMANCE ; NUMBER ; DATABASE ; SIGNALING PATHWAY ; PREDICTION ; ORGANIZATION ; DOMAINS ; signaling ; RE ; databases ; FUNCTIONAL-CHARACTERIZATION ; methods ; SIGNATURE ; microbiology ; ENGLAND ; PREDICT ; biotechnology ; FALSE DISCOVERY RATE ; POSITION
    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
    Type of Publication: Journal article published
    PubMed ID: 18676972
    Signatur Availability
    BibTip Others were also interested in ...
  • 3
    Keywords: CELL ; Germany ; MODEL ; MODELS ; PATHWAY ; ALGORITHM ; ALGORITHMS ; GENE ; GENES ; microarray ; BIOLOGY ; microarrays ; PHENOTYPE ; REPRESENTATION ; signaling ; SOFTWARE ; methods ; SCREEN ; PROFILES ; CANDIDATE ; microbiology ; ENGLAND ; SET ; NOV ; PROFILE ; biotechnology
    Abstract: Nested effects models (NEMs) are a class of probabilistic models introduced to analyze the effects of gene perturbation screens visible in high-dimensional phenotypes like microarrays or cell morphology. NEMs reverse engineer upstream/downstream relations of cellular signaling cascades. NEMs take as input a set of candidate pathway genes and phenotypic profiles of perturbing these genes. NEMs return a pathway structure explaining the observed perturbation effects. Here, we describe the package nem, an open-source software to efficiently infer NEMs from data. Our software implements several search algorithms for model fitting and is applicable to a wide range of different data types and representations. The methods we present summarize the current state-of-the-art in NEMs
    Type of Publication: Journal article published
    PubMed ID: 18718939
    Signatur Availability
    BibTip Others were also interested in ...
  • 4
    Keywords: ENVIRONMENT ; EXPRESSION ; Germany ; INFORMATION ; TOOL ; GENE ; GENES ; GENOME ; microarray ; DNA ; MICROARRAY DATA ; PRODUCT ; bioinformatics ; PROJECT ; CLUSTER ; GENE-PRODUCT ; review ; RE ; PRODUCTS ; SOFTWARE ; ANNOTATION ; CLUSTER-ANALYSIS ; FUNCTIONAL ANNOTATION ; GO ; analysis ; methods ; DNA-MICROARRAY ; function ; EVALUATE ; E ; TOOLS ; microbiology ; -
    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 respect to their expression, but also with respect to their functional annotation which can be obtained from Gene Ontology ( GO). Results: 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. Conclusion: GOSim is implemented as a package for the statistical computing environment R and is distributed under GPL within the CRAN project
    Type of Publication: Journal article published
    PubMed ID: 17519018
    Signatur Availability
    BibTip Others were also interested in ...
  • 5
    Keywords: CANCER ; CANCER CELLS ; CELLS ; CELL ; COMBINATION ; Germany ; human ; MODEL ; MODELS ; PATHWAY ; PATHWAYS ; ALGORITHMS ; GENE ; GENES ; microarray ; RNA ; COMPLEX ; COMPLEXES ; DNA ; BIOLOGY ; BREAST ; breast cancer ; BREAST-CANCER ; microarrays ; SIGNALING PATHWAY ; SIGNALING PATHWAYS ; CANCER-CELLS ; signaling ; review ; INTERFERENCE ; SIGNALING NETWORK ; signaling networks ; Nested effects models ; Perturbation data ; Signaling pathway inference
    Abstract: Targeted gene perturbations have become a major tool to gain insight into complex cellular processes. In combination with the measurement of downstream effects via DNA microarrays, this approach can be used to gain insight into signaling pathways. Nested Effects Models were first introduced by Markowetz et al. as a probabilistic method to reverse engineer signaling cascades based on the nested structure of downstream perturbation effects. The basic framework was substantially extended later on by Frohlich et al., Markowetz et al., and Tresch and Markowetz. In this paper, we present a review of the complete methodology with a detailed comparison of so far proposed algorithms on a qualitative and quantitative level. As an application, we present results on estimating the signaling network between 13 genes in the ER-a pathway of human MCF-7 breast cancer cells. Comparison with the literature shows a substantial overlap
    Type of Publication: Journal article published
    PubMed ID: 19358219
    Signatur Availability
    BibTip Others were also interested in ...
  • 6
    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
    Signatur Availability
    BibTip Others were also interested in ...
  • 7
    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
    Signatur Availability
    BibTip Others were also interested in ...
  • 8
    Keywords: CANCER ; tumor ; evaluation ; Germany ; MODEL ; MODELS ; DIAGNOSIS ; SUPPORT ; DISEASE ; DISEASES ; SAMPLE ; SAMPLES ; PATIENT ; MARKER ; BREAST ; breast cancer ; BREAST-CANCER ; VECTOR ; WOMEN ; MARKERS ; MASS-SPECTROMETRY ; CANCER-PATIENTS ; NETHERLANDS ; CHROMATOGRAPHY ; LIQUID-CHROMATOGRAPHY ; PREDICTION ; sensitivity ; specificity ; CLINICAL-DIAGNOSIS ; CANCER PATIENTS ; HEALTHY ; AFFINITY-CHROMATOGRAPHY ; URINE ; PERFORMANCE LIQUID-CHROMATOGRAPHY ; SERUM ; CHEMISTRY ; NUCLEOSIDES ; PHASE ; metabolomics ; SET ; biological markers ; support vector machine ; TUMOR-MARKERS ; affinity chromatography ; chromatography with ultraviolett detection (HPLC-UV) ; high performance liquid ; k-nearest-neighbor classifier (k-NN) ; NEURAL-NETWORK ; PSEUDOURIDINE ; RNA MODIFICATION ; Support Vector Machine (SVM) ; TOF-MS ; TUMOR MARKERS ; URINARY MODIFIED NUCLEOSIDES
    Abstract: It is known that patients suffering from cancer diseases excrete increased amounts of modified nucleosides with their urine. Especially methylated nucleosides have been proposed to be potential tumor markers for early diagnosis of cancer. For determination of nucleosides in randomly collected urine samples, the nucleosides were extracted using affinity chromatography and then analyzed via reversed phase high-performance liquid chromatography (HPLC) with UV-detection. Eleven nucleosides were quantified in urine samples from 51 breast cancer patients and 65 healthy women. The measured concentrations were used to train a Support Vector Machine (SVM) and a k-nearest-neighbor classifier (k-NN) to discriminate between healthy control subjects and patients suffering from breast cancer. Evaluations of the learned models by computing the leave-one-out error and the prediction error on an independent test set of 29 subjects (15 healthy, 14 breast cancer patients) showed that by using the eleven nucleosides, the occurrence of breast cancer could be forecasted with 86% specificity and 94% sensitivity when using an SVM and 86% for both specificity and sensitivity with the k-NN model. (C) 2008 Elsevier B.V. All rights reserved
    Type of Publication: Journal article published
    PubMed ID: 18501242
    Signatur Availability
    BibTip Others were also interested in ...
  • 9
    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
    Signatur Availability
    BibTip Others were also interested in ...
  • 10
    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
    Signatur Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...