In diesem Semester wird die im WiSe 2022/23 erfolgreich gestartete Vortragsreihe 威尼斯赌博游戏_威尼斯赌博app-【官网】ical Information Sciences fortgesetzt. Renommierte Wissenschaftlerinnen und Wissenschaftler unterschiedlicher Fachdisziplinen und Forschungsstandorte geben jeden Donnerstag ab 16:00 Uhr Einblicke in aktuelle Fragestellungen und Anwendungsgebiete des breiten Forschungsfeldes 威尼斯赌博游戏_威尼斯赌博app-【官网】ical Information Sciences.

High-throughput technologies are becoming increasingly important in discovering prognostic biomarkers and in identifying novel drug targets. With various prognostic molecular signatures, cancer serves as a paradigmatic example of the utility of high-throughput data in identifying prognostic biomarkers, often distilled into relatively short gene lists. Such gene lists can be obtained as a set of features (genes) that are important for the decisions of a Machine Learning (ML) method applied to high-dimensional gene expression data. This high-dimensional data can be structured by graphs, which are very common in the biomedical domain. For instance, a patient can be represented by a protein-protein interaction (PPI) network where the nodes contain the patient-specific omics features. Graph Neural Networks (GNNs) can classify these graph-based representations.?

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?Explanation methods applied to GNNs produce explanations of individual predictions that can be utilized to construct patient-specific subnetworks. When aggregated across patients, these explanations enable model-wide feature selection. In this talk, I will discuss both aspects and present a methodology to: (i) derive patient-specific subnetworks that are potentially valuable for precision medicine approaches, and (ii) systematically and quantitatively analyze the stability, impact on classification performance, and biological interpretability of the model-wide selected feature sets. Finally, I will present our Ensemble-GNN approach for classifying graph-structured data, which can be used to deploy federated, ensemble-based GNNs in Python.