Logo des Repositoriums

Auflistung nach Autor:in "Scholz, Matthias"

1 - 2 von 2
  • Konferenzbeitrag
    Independent components analysis of starch deficient pgm mutants
    (German Conference on Bioinformatics 2004, GCB 2004, 2004) Scholz, Matthias; Gibon, Yves; Stitt, Mark; Selbig, Joachim
    Changes in enzymatic activities in response to carbon starvation were investigated in Arabidopsis thaliana in two distinct experiments. One compares the Columbia ecotype (Col-0) and its starch deficient pgm mutant (plastidial phosphoglucomutase), the other investigates the enzymatic activities of Col-0 under extended night conditions. A classical technique for detecting and visualizing relevant information from the measured data is principal component analysis (PCA). We show that independent component analysis (ICA) is more suitable for our questions and the results are more precise than those obtained with PCA. This higher informative power is only achieved when ICA is combined with suitable pre-processing and evaluation criteria. It is essential to first reduce the dimensionality of the data set, using PCA. The number of principal components determines the quality of ICA significantly, therefore we propose a criterion for estimating the optimal dimension automatically. The measure of kurtosis is used to sort the extracted components. We found that ICA could detect on the one hand the time component of the extended night experiment, and on the other hand a discriminating component in the pgm mutant experiment. In both components the most important enzymes were the same, confirming the carbon starvation phenotype in the mutant.
  • Textdokument
    Pattern based decision tree analysis for risk detection in smart cities
    (INFORMATIK 2017, 2017) Scholz, Matthias; Piller:, Gunther
    Increasing amounts of data on living environments and human interactions are becoming available. Their potential for valuable services improving the wellbeing of individuals is large and growing. This calls for an investigation of algorithms and system architectures that support possible use cases. In this paper we outline how pattern based decision tree analyses can be applied to the identification of risks caused by time-dependent effects from multiple influencing factors. For this purpose we apply the method to open data on car accidents and weather conditions. We also show how such systems can take advantage from up-to-date in-memory technology.