@article{MenningGrasnickEwaldetal.2018,
  author    = {Menning, Axel and Grasnick, Bastien M. and Ewald, Benedikt and Dobrigkeit, Franziska and Nicolai, Claudia},
  title     = {Verbal focus shifts},
  series = {Design Studies},
  volume    = {57},
  journal   = {Design Studies},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0142-694X},
  doi       = {10.1016/j.destud.2018.03.003},
  pages     = {135 -- 155},
  year      = {2018},
  abstract  = {Previous studies on design behaviour indicate that focus shifts positively influence ideational productivity. In this study we want to take a closer look at how these focus shifts look on the verbal level. We describe a mutually influencing relationship between mental focus shifts and verbal low coherent statements. In a case study based on the DTRS11 dataset we identify 297 low coherent statements via a combined topic modelling and manual approach. We introduce a categorization of the different instances of low coherent statements. The results indicate that designers tend to shift topics within an existing design issue instead of completely disrupting it. (C) 2018 Elsevier Ltd. All rights reserved.},
  language  = {en}
}
@article{PerscheidGrasnickUflacker2019,
  author    = {Perscheid, Cindy and Grasnick, Bastien and Uflacker, Matthias},
  title     = {Integrative Gene Selection on Gene Expression Data},
  series = {Journal of Integrative Bioinformatics},
  volume    = {16},
  journal   = {Journal of Integrative Bioinformatics},
  number    = {1},
  publisher = {De Gruyter},
  address   = {Berlin},
  issn      = {1613-4516},
  doi       = {10.1515/jib-2018-0064},
  pages     = {17},
  year      = {2019},
  abstract  = {The advance of high-throughput RNA-Sequencing techniques enables researchers to analyze the complete gene activity in particular cells. From the insights of such analyses, researchers can identify disease-specific expression profiles, thus understand complex diseases like cancer, and eventually develop effective measures for diagnosis and treatment. The high dimensionality of gene expression data poses challenges to its computational analysis, which is addressed with measures of gene selection. Traditional gene selection approaches base their findings on statistical analyses of the actual expression levels, which implies several drawbacks when it comes to accurately identifying the underlying biological processes. In turn, integrative approaches include curated information on biological processes from external knowledge bases during gene selection, which promises to lead to better interpretability and improved predictive performance. Our work compares the performance of traditional and integrative gene selection approaches. Moreover, we propose a straightforward approach to integrate external knowledge with traditional gene selection approaches. We introduce a framework enabling the automatic external knowledge integration, gene selection, and evaluation. Evaluation results prove our framework to be a useful tool for evaluation and show that integration of external knowledge improves overall analysis results.},
  language  = {en}
}