Search Results - "BEISSBARTH, Tim"

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    Stable feature selection utilizing Graph Convolutional Neural Network and Layer-wise Relevance Propagation for biomarker discovery in breast cancer by Chereda, Hryhorii, Leha, Andreas, Beißbarth, Tim

    Published in Artificial intelligence in medicine (01-05-2024)
    “…High-throughput technologies are becoming increasingly important in discovering prognostic biomarkers and in identifying novel drug targets. With Mammaprint,…”
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    Journal Article
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    GOstat: find statistically overrepresented Gene Ontologies within a group of genes by Beißbarth, Tim, Speed, Terence P.

    Published in Bioinformatics (12-06-2004)
    “…Modern experimental techniques, as for example DNA microarrays, as a result usually produce a long list of genes, which are potentially interesting in the…”
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    Text mining-based word representations for biomedical data analysis and protein-protein interaction networks in machine learning tasks by Alachram, Halima, Chereda, Hryhorii, Beißbarth, Tim, Wingender, Edgar, Stegmaier, Philip

    Published in PloS one (15-10-2021)
    “…Biomedical and life science literature is an essential way to publish experimental results. With the rapid growth of the number of new publications, the amount…”
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    Explaining decisions of graph convolutional neural networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer by Chereda, Hryhorii, Bleckmann, Annalen, Menck, Kerstin, Perera-Bel, Júlia, Stegmaier, Philip, Auer, Florian, Kramer, Frank, Leha, Andreas, Beißbarth, Tim

    Published in Genome medicine (11-03-2021)
    “…Contemporary deep learning approaches show cutting-edge performance in a variety of complex prediction tasks. Nonetheless, the application of deep learning in…”
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    CODEX: COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations by Schrod, Stefan, Zacharias, Helena U, Beißbarth, Tim, Hauschild, Anne-Christin, Altenbuchinger, Michael

    Published in Bioinformatics (Oxford, England) (28-06-2024)
    “…Abstract Motivation High-throughput screens (HTS) provide a powerful tool to decipher the causal effects of chemical and genetic perturbations on cancer cell…”
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    Impact of RNA degradation on gene expression profiling by Opitz, Lennart, Salinas-Riester, Gabriela, Grade, Marian, Jung, Klaus, Jo, Peter, Emons, Georg, Ghadimi, B Michael, Beissbarth, Tim, Gaedcke, Jochen

    Published in BMC genomics (09-08-2010)
    “…Gene expression profiling is a highly sensitive technique which is used for profiling tumor samples for medical prognosis. RNA quality and degradation…”
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    pwOmics: an R package for pathway-based integration of time-series omics data using public database knowledge by Wachter, Astrid, Beißbarth, Tim

    Published in Bioinformatics (15-09-2015)
    “…Characterization of biological processes is progressively enabled with the increased generation of omics data on different signaling levels. Here we present a…”
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    Differences in the Reponses to Apheresis Therapy of Patients With 3 Histopathologically Classified Immunopathological Patterns of Multiple Sclerosis by Stork, Lidia, Ellenberger, David, Beißbarth, Tim, Friede, Tim, Lucchinetti, Claudia F, Brück, Wolfgang, Metz, Imke

    Published in JAMA neurology (01-04-2018)
    “…Plasma exchange and immunoadsorption are second-line apheresis therapies for patients experiencing multiple sclerosis relapses. Early active multiple sclerosis…”
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    Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification by Pfeifer, Bastian, Chereda, Hryhorii, Martin, Roman, Saranti, Anna, Clemens, Sandra, Hauschild, Anne-Christin, Beißbarth, Tim, Holzinger, Andreas, Heider, Dominik

    Published in Bioinformatics (Oxford, England) (01-11-2023)
    “…Abstract Summary Federated learning enables collaboration in medicine, where data is scattered across multiple centers without the need to aggregate the data…”
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    Working with Ontologies by Kramer, Frank, Beißbarth, Tim

    “…Ontologies are powerful and popular tools to encode data in a structured format and manage knowledge. A large variety of existing ontologies offer users access…”
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    Comparative study on gene set and pathway topology-based enrichment methods by Bayerlová, Michaela, Jung, Klaus, Kramer, Frank, Klemm, Florian, Bleckmann, Annalen, Beißbarth, Tim

    Published in BMC bioinformatics (22-10-2015)
    “…Enrichment analysis is a popular approach to identify pathways or sets of genes which are significantly enriched in the context of differentially expressed…”
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    A comparative study of RNA-Seq and microarray data analysis on the two examples of rectal-cancer patients and Burkitt Lymphoma cells by Wolff, Alexander, Bayerlová, Michaela, Gaedcke, Jochen, Kube, Dieter, Beißbarth, Tim

    Published in PloS one (16-05-2018)
    “…Pipeline comparisons for gene expression data are highly valuable for applied real data analyses, as they enable the selection of suitable analysis strategies…”
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    GOSim--an R-package for computation of information theoretic GO similarities between terms and gene products by Fröhlich, Holger, Speer, Nora, Poustka, Annemarie, Beissbarth, Tim

    Published in BMC bioinformatics (22-05-2007)
    “…With the increased availability of high throughput data, such as DNA microarray data, researchers are capable of producing large amounts of biological data…”
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    Discriminating malignant and benign clinical T1 renal masses on computed tomography: A pragmatic radiomics and machine learning approach by Uhlig, Johannes, Biggemann, Lorenz, Nietert, Manuel M., Beißbarth, Tim, Lotz, Joachim, Kim, Hyun S., Trojan, Lutz, Uhlig, Annemarie

    Published in Medicine (Baltimore) (01-04-2020)
    “…The aim of this study was to discriminate malignant and benign clinical T1 renal masses on routinely acquired computed tomography (CT) images using radiomics…”
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    Constructing temporal regulatory cascades in the context of development and cell differentiation by Daou, Rayan, Beißbarth, Tim, Wingender, Edgar, Gültas, Mehmet, Haubrock, Martin

    Published in PloS one (10-04-2020)
    “…Cell differentiation is a complex process orchestrated by sets of regulators precisely appearing at certain time points, resulting in regulatory cascades that…”
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