@article{KoenigerKuerten2017,
  author    = {Koeniger, Tobias and Kuerten, Stefanie},
  title     = {Splitting the "unsplittable": Dissecting resident and infiltrating macrophages in experimental autoimmune encephalomyelitis},
  series = {International Journal of Molecular Sciences},
  volume    = {18},
  journal   = {International Journal of Molecular Sciences},
  number    = {10},
  issn      = {1422-0067},
  doi       = {10.3390/ijms18102072},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-285067},
  year      = {2017},
  abstract  = {Macrophages predominate the inflammatory landscape within multiple sclerosis (MS) lesions, not only regarding cellularity but also with respect to the diverse functions this cell fraction provides during disease progression and remission. Researchers have been well aware of the fact that the macrophage pool during central nervous system (CNS) autoimmunity consists of a mixture of myeloid cells. Yet, separating these populations to define their unique contribution to disease pathology has long been challenging due to their similar marker expression. Sophisticated lineage tracing approaches as well as comprehensive transcriptome analysis have elevated our insight into macrophage biology to a new level enabling scientists to dissect the roles of resident (microglia and non-parenchymal macrophages) and infiltrating macrophages with unprecedented precision. To do so in an accurate way, researchers have to know their toolbox, which has been filled with diverse, discriminating approaches from decades of studying neuroinflammation in animal models. Every method has its own strengths and weaknesses, which will be addressed in this review. The focus will be on tools to manipulate and/or identify different macrophage subgroups within the injured murine CNS.},
  language  = {en}
}
@article{StaigerCadotKooteretal.2012,
  author    = {Staiger, Christine and Cadot, Sidney and Kooter, Raul and Dittrich, Marcus and M{\"u}ller, Tobias and Klau, Gunnar W. and Wessels, Lodewyk F. A.},
  title     = {A Critical Evaluation of Network and Pathway-Based Classifiers for Outcome Prediction in Breast Cancer},
  series = {PLoS One},
  volume    = {7},
  journal   = {PLoS One},
  number    = {4},
  doi       = {10.1371/journal.pone.0034796},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-131323},
  pages     = {e34796},
  year      = {2012},
  abstract  = {Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single genes classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single genes classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single genes classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single genes sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single genes classifiers for predicting outcome in breast cancer.},
  language  = {en}
}