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Auflistung nach Autor:in "Kel, Alexander"

1 - 3 von 3
  • Konferenzbeitrag
    Composite module analyst: A fitness-based tool for prediction of transcription regulation
    (German Conference on Bioinformatics 2005 (GCB 2005), 2005) Kel, Alexander; Konovalova, Tatiana; Waleev, Tagir; Cheremushkin, Evgeny; Kel-Margoulis, Olga; Wingender, Edgar
    Functionally related genes involved in the same molecular-genetic, biochemical, or physiological process are often regulated coordinately Such regulation is provided by precisely organized binding of a multiplicity of special proteins (transcription factors) to their target sites (cis-elements) in regulatory regions of genes. Cis-element combinations provide a structural basis for the generation of unique patterns of gene expression. Here we present a new approach for defining promoter models based on composition of transcription factor binding sites and their pairs. We utilize a multicomponent fitness function for selection of that promoter model fitting best to the observed gene expression profile. We demonstrate examples of successful application of the fitness function with the help of a genetic algorithm for the analysis of functionally related or co-expressed genes as well as testing on simulated data.
  • Konferenzbeitrag
    From composite patters to pathways – Prediction of key regulators of gene expression
    (German Conference on Bioinformatics 2004, GCB 2004, 2004) Kel, Alexander; Voss, Nico; Konovalova, Tatyana; Tchekmenev, Dmitri; Wabnitz, Philipp; Kelmargoulis, Olga; Wingender, Edgar
  • Konferenzbeitrag
    Supervised posteriors for DNA-motif classification
    (German conference on bioinformatics – GCB 2007, 2007) Grau, Jan; Keilwagen, Jens; Kel, Alexander; Grosse, Ivo; Posch, Stefan
    Markov models have been proposed for the classification of DNA-motifs using generative approaches for parameter learning. Here, we propose to apply the discriminative paradigm for this problem and study two different priors to facilitate parameter estimation using the maximum supervised posterior. Considering seven sets of eukaryotic transcription factor binding sites we find this approach to be superior employing area under the ROC curve and false positive rate as performance criterion, and better in general using sensitivity. In addition, we discuss potential reasons for the improved performance.