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A new physarum learner for network structure learning from biomedical data
Schön, T.; Stetter, M.; Tomé, A. M.; Lang, E. W.A novel structure learning algorithm for Bayesian Networks based on a Physarum Learner is presented. The length of the connections within an initially fully connected Physarum-Maze is taken as the inverse Pearson correlation coefficient between the connected nodes. The Physarum Learner then estimates the shortest indirect paths between each pair of nodes. In each iteration, a score of the surviving edges is inc...
Knowledge-based gene expression classification via matrix factorization
Schachtner, R.; Lutter, D.; Knollmüller, P.; Tomé, A. M.; Theis, F. J.; Schmitz, G.; Stetter, M.; Gómez Vilda, P.; Lang, E. W.Motivation: Modern machine learning methods based on matrix decomposition techniques, like independent component analysis (ICA) or non-negative matrix factorization (NMF), provide new and efficient analysis tools which are currently explored to analyze gene expression profiles. These exploratory feature extraction techniques yield expression modes (ICA) or metagenes (NMF). These extracted features are considere...
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