Daniela Baumgartner

Learn More
Machine learning has a great potential to mine potential markers from high-dimensional metabolic data without any a priori knowledge. Exemplarily, we investigated metabolic patterns of three severe metabolic disorders, PAHD, MCADD, and 3-MCCD, on which we constructed classification models for disease screening and diagnosis using a decision tree paradigm(More)
MOTIVATION During the Bavarian newborn screening programme all newborns have been tested for about 20 inherited metabolic disorders. Owing to the amount and complexity of the generated experimental data, machine learning techniques provide a promising approach to investigate novel patterns in high-dimensional metabolic data which form the source for(More)
BACKGROUND Despite successful surgical correction, morbidity of patients with coarctation of the aorta is increased. It is well known that these patients have impaired elastic properties of the prestenotic arteries. To find out whether these abnormalities are primarily present or develop later, we studied 17 newborns before and early after surgical repair.(More)
The search and validation of novel disease biomarkers requires the complementary power of professional study planning and execution, modern profiling technologies and related bioinformatics tools for data analysis and interpretation. Biomarkers have considerable impact on the care of patients and are urgently needed for advancing diagnostics, prognostics(More)
Tandem mass spectrometry is a promising new screening technology which permits screening within one analytical run not only for phenylketonuria (PKU) but also for a wide range of other metabolic disorders in newborns. We investigated two symbolic supervised machine learning techniques-logistic regression analysis (LRA) and decision trees (DT), where the(More)
Mutations in the human FBN1 gene are known to be associated with the Marfan syndrome, an autosomal dominant inherited multi-systemic connective tissue disorder. However, in the absence of solid genotype-phenotype correlations, the identification of an FBN1 mutation has only little prognostic value. We propose a bioinformatics framework for the mutated FBN1(More)
Cardiovascular disease is the leading cause of death worldwide. Evidence points towards an unfavorable cardiovascular risk profile of former preterm infants in adolescence and adulthood. The aim of this study was to determine whether cardiovascular risk predictors are detectable in former very preterm infants at a preschool age. Five- to seven-year-old(More)
  • 1