Michael Netzer

Summary

Affiliation: Medical Informatics and Technology
Country: Austria

Publications

  1. doi request reprint A network-based feature selection approach to identify metabolic signatures in disease
    Michael Netzer
    Research Group for Clinical Bioinformatics, Institute of Electrical and Biomedical Engineering, University for Health Sciences, Medical Informatics and Technology, 6060 Hall in Tyrol, Austria
    J Theor Biol 310:216-22. 2012
  2. pmc Bioinformatic-driven search for metabolic biomarkers in disease
    Christian Baumgartner
    Research Group for Clinical Bioinformatics, Institute of Electrical, Electronic and Bioengineering, University for Health Sciences, Medical Informatics and Technology UMIT, Hall in Tirol, Austria
    J Clin Bioinforma 1:2. 2011
  3. pmc Profiling the human response to physical exercise: a computational strategy for the identification and kinetic analysis of metabolic biomarkers
    Michael Netzer
    Research Group for Clinical Bioinformatics, Institute of Electrical, Electronic and Bioengineering, UMIT, 6060 Hall in Tirol, Austria
    J Clin Bioinforma 1:34. 2011
  4. pmc Effects of pooling samples on the performance of classification algorithms: a comparative study
    Kanthida Kusonmano
    Institute for Bioinformatics and Translational Research, UMIT, 6060 Hall in Tyrol, Austria
    ScientificWorldJournal 2012:278352. 2012
  5. pmc A network-based approach to classify the three domains of life
    Laurin A J Mueller
    Institute for Bioinformatics and Translational Research, Department of Biomedical Sciences and Engineering, University for Health Sciences, Medical Informatics and Technology UMIT, Austria
    Biol Direct 6:53. 2011

Detail Information

Publications5

  1. doi request reprint A network-based feature selection approach to identify metabolic signatures in disease
    Michael Netzer
    Research Group for Clinical Bioinformatics, Institute of Electrical and Biomedical Engineering, University for Health Sciences, Medical Informatics and Technology, 6060 Hall in Tyrol, Austria
    J Theor Biol 310:216-22. 2012
    ..Using our network-based feature selection approach we identified highly discriminating metabolites for obesity (F-score >0.85, accuracy >85%), some of which could be verified by the literature...
  2. pmc Bioinformatic-driven search for metabolic biomarkers in disease
    Christian Baumgartner
    Research Group for Clinical Bioinformatics, Institute of Electrical, Electronic and Bioengineering, University for Health Sciences, Medical Informatics and Technology UMIT, Hall in Tirol, Austria
    J Clin Bioinforma 1:2. 2011
    ..This review demonstrates that clinical bioinformatics has evolved into an essential element of biomarker discovery, translating new innovations and successes in profiling technologies and bioinformatics to clinical application...
  3. pmc Profiling the human response to physical exercise: a computational strategy for the identification and kinetic analysis of metabolic biomarkers
    Michael Netzer
    Research Group for Clinical Bioinformatics, Institute of Electrical, Electronic and Bioengineering, UMIT, 6060 Hall in Tirol, Austria
    J Clin Bioinforma 1:34. 2011
    ..abstract:..
  4. pmc Effects of pooling samples on the performance of classification algorithms: a comparative study
    Kanthida Kusonmano
    Institute for Bioinformatics and Translational Research, UMIT, 6060 Hall in Tyrol, Austria
    ScientificWorldJournal 2012:278352. 2012
    ..Guidelines are derived to identify an optimal pooling scheme for obtaining adequate predictive power and, hence, to motivate a study design that meets best experimental objectives and budgetary conditions, including time constraints...
  5. pmc A network-based approach to classify the three domains of life
    Laurin A J Mueller
    Institute for Bioinformatics and Translational Research, Department of Biomedical Sciences and Engineering, University for Health Sciences, Medical Informatics and Technology UMIT, Austria
    Biol Direct 6:53. 2011
    ..Moreover, we combine the two groups to perform a feature selection to estimate the structural features with the highest classification ability in order to optimize the classification performance...