@article{FeherLisecRoemischMargletal.2014,
  author    = {Feher, Kristen and Lisec, Jan and Roemisch-Margl, Lilla and Selbig, Joachim and Gierl, Alfons and Piepho, Hans-Peter and Nikoloski, Zoran and Willmitzer, Lothar},
  title     = {Deducing hybrid performance from parental metabolic profiles of young primary roots of maize by using a multivariate diallel approach},
  series = {PLoS one},
  volume    = {9},
  journal   = {PLoS one},
  number    = {1},
  publisher = {PLoS},
  address   = {San Fransisco},
  issn      = {1932-6203},
  doi       = {10.1371/journal.pone.0085435},
  pages     = {9},
  year      = {2014},
  language  = {en}
}
@article{JuerchottGuoCatchpoleetal.2011,
  author    = {Juerchott, Kathrin and Guo, Ke-Tai and Catchpole, Gareth and Feher, Kristen and Willmitzer, Lothar and Schichor, Christian and Selbig, Joachim},
  title     = {Comparison of metabolite profiles in U87 glioma cells and mesenchymal stem cells},
  series = {Biosystems : journal of biological and information processing sciences},
  volume    = {105},
  journal   = {Biosystems : journal of biological and information processing sciences},
  number    = {2},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0303-2647},
  doi       = {10.1016/j.biosystems.2011.05.005},
  pages     = {130 -- 139},
  year      = {2011},
  abstract  = {Gas chromatography-mass spectrometry (GC-MS) profiles were generated from U87 glioma cells and human mesenchymal stem cells (hMSC). 37 metabolites representing glycolysis intermediates, TCA cycle metabolites, amino acids and lipids were selected for a detailed analysis. The concentrations of these. metabolites were compared and Pearson correlation coefficients were used to calculate the relationship between pairs of metabolites. Metabolite profiles and correlation patterns differ significantly between the two cell lines. These profiles can be considered as a signature of the underlying biochemical system and provide snap-shots of the metabolism in mesenchymal stem cells and tumor cells.},
  language  = {en}
}
@article{FeherWhelanMueller2011,
  author    = {Feher, Kristen and Whelan, James and M{\"u}ller, Samuel},
  title     = {Assessing modularity using a random matrix theory approach},
  series = {Statistical applications in genetics and molecular biology},
  volume    = {10},
  journal   = {Statistical applications in genetics and molecular biology},
  number    = {1},
  publisher = {De Gruyter},
  address   = {Berlin},
  issn      = {2194-6302},
  doi       = {10.2202/1544-6115.1667},
  pages     = {36},
  year      = {2011},
  abstract  = {Random matrix theory (RMT) is well suited to describing the emergent properties of systems with complex interactions amongst their constituents through their eigenvalue spectrums. Some RMT results are applied to the problem of clustering high dimensional biological data with complex dependence structure amongst the variables. It will be shown that a gene relevance or correlation network can be constructed by choosing a correlation threshold in a principled way, such that it corresponds to a block diagonal structure in the correlation matrix, if such a structure exists. The structure is then found using community detection algorithms, but with parameter choice guided by RMT predictions. The resulting clustering is compared to a variety of hierarchical clustering outputs and is found to the most generalised result, in that it captures all the features found by the other considered methods.},
  language  = {en}
}