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Auflistung nach Schlagwort "Bayesian networks"

1 - 2 von 2
  • Konferenzbeitrag
    Feature based representation and detection of transcription factor binding sites
    (German Conference on Bioinformatics 2004, GCB 2004, 2004) Pudimat, Rainer; Schukat-Talamazzini, Ernst-Günter; Backofen, Rolf
    The prediction of transcription factor binding sites is an important problem, since it reveals information about the transcriptional regulation of genes. A commonly used representation of these sites are position specific weight matrices which show weak predictive power. We introduce a feature-based modelling approach, which is able to deal with various kind of biological properties of binding sites and models them via Bayesian belief networks. The presented results imply higher model accuracy in contrast to the PSSM approach.
  • Textdokument
    Influence Estimation In Multi-Step Process Chains Using Quantum Bayesian Networks
    (INFORMATIK 2022, 2022) Selch,Maximilian; Müssig,Daniel; Hänel,Albrecht; Lässig,Jörg; Ihlenfeldt,Steffen
    Digital representatives of physical assets and process steps play a decisive role in analysing properties and evaluating the quality of the process. So-called digital twins acquire all relevant planning and process data, which provide the basis, for example, to investigate path accuracies in manufacturing. Each single process step aims to perform an ideal machining after the specification of a target geometry. However, the practical implementation of a step usually shows deviations from the targeted shape. The machine-learning based method of probabilistic Bayesian networks enables the quality estimation of the holistic process chain as well as improvements by targeted considerations of single steps and influence factors. However, the handling of large-scale Bayesian networks requires a high computational effort, whereas the processing with quantum algorithms holds potential improvements in storage and performance. Based on the issue of path accuracy, this paper considers the modelling and influence estimation for a milling operation including experiments on superconducting quantum hardware.