@misc{SteinfathGaertnerLisecetal.2009,
  author    = {Steinfath, Matthias and G{\"a}rtner, Tanja and Lisec, Jan and Meyer, Rhonda C. and Altmann, Thomas and Willmitzer, Lothar and Selbig, Joachim},
  title     = {Prediction of hybrid biomass in Arabidopsis thaliana by selected parental SNP and metabolic markers},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1324},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-43111},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-431115},
  pages     = {9},
  year      = {2009},
  abstract  = {A recombinant inbred line (RIL) population, derived from two Arabidopsis thaliana accessions, and the corresponding testcrosses with these two original accessions were used for the development and validation of machine learning models to predict the biomass of hybrids. Genetic and metabolic information of the RILs served as predictors. Feature selection reduced the number of variables (genetic and metabolic markers) in the models by more than 80\% without impairing the predictive power. Thus, potential biomarkers have been revealed. Metabolites were shown to bear information on inherited macroscopic phenotypes. This proof of concept could be interesting for breeders. The example population exhibits substantial mid-parent biomass heterosis. The results of feature selection could therefore be used to shed light on the origin of heterosis. In this respect, mainly dominance effects were detected.},
  language  = {en}
}
@article{SteinfathGaertnerLisecetal.2009,
  author    = {Steinfath, Matthias and G{\"a}rtner, Tanja and Lisec, Jan and Meyer, Rhonda Christiane and Altmann, Thomas and Willmitzer, Lothar and Selbig, Joachim},
  title     = {Prediction of hybrid biomass in Arabidopsis thaliana by selected parental SNP and metabolic markers},
  series = {Theoretical and applied genetics : TAG ; international journal of plant breeding research},
  volume    = {120},
  journal   = {Theoretical and applied genetics : TAG ; international journal of plant breeding research},
  publisher = {Springer},
  address   = {Berlin},
  issn      = {0040-5752},
  doi       = {10.1007/s00122-009-1191-2},
  pages     = {239 -- 247},
  year      = {2009},
  abstract  = {A recombinant inbred line (RIL) population, derived from two Arabidopsis thaliana accessions, and the corresponding testcrosses with these two original accessions were used for the development and validation of machine learning models to predict the biomass of hybrids. Genetic and metabolic information of the RILs served as predictors. Feature selection reduced the number of variables (genetic and metabolic markers) in the models by more than 80\% without impairing the predictive power. Thus, potential biomarkers have been revealed. Metabolites were shown to bear information on inherited macroscopic phenotypes. This proof of concept could be interesting for breeders. The example population exhibits substantial mid-parent biomass heterosis. The results of feature selection could therefore be used to shed light on the origin of heterosis. In this respect, mainly dominance effects were detected.},
  language  = {en}
}