@article{KabaMaierSchliebeOhleretal.2015,
  author    = {Kaba, Hani E. J. and Maier, Natalia and Schliebe-Ohler, Nicole and Mayer, Yvonne and Mueller, Peter P. and van den Heuvel, Joop and Schuchhardt, Johannes and Hanack, Katja and Bilitewski, Ursula},
  title     = {Identification of whole pathogenic cells by monoclonal antibodies generated against a specific peptide from an immunogenic cell wall protein},
  series = {Journal of microbiological methods},
  volume    = {108},
  journal   = {Journal of microbiological methods},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0167-7012},
  doi       = {10.1016/j.mimet.2014.11.003},
  pages     = {61 -- 69},
  year      = {2015},
  abstract  = {We selected the immunogenic cell wall beta-(1,3)-glucosyltransferase Bgl2p from Candida albicans as a target protein for the production of antibodies. We identified a unique peptide sequence in the protein and generated monoclonal anti- C. albicans Bgl2p antibodies, which bound in particular to whole C. albicans cells.},
  language  = {en}
}
@inproceedings{MotalnSchuchhardtStecetal.2014,
  author    = {Motaln, Helena and Schuchhardt, Johannes and Stec, Karol and Breznik, Barbara and Ulrich, Henning and Lah, Tamara T.},
  title     = {Paradoxical role of mesenchymal stem cells in the glioblastoma microenvironment},
  series = {Anticancer research : international journal of cancer research and treatment},
  volume    = {34},
  booktitle = {Anticancer research : international journal of cancer research and treatment},
  number    = {10},
  publisher = {International Institute of Anticancer Research},
  address   = {Athens},
  issn      = {0250-7005},
  pages     = {6071 -- 6072},
  year      = {2014},
  language  = {en}
}
@article{BauerHerwigLienhardetal.2021,
  author    = {Bauer, Chris and Herwig, Ralf and Lienhard, Matthias and Prasse, Paul and Scheffer, Tobias and Schuchhardt, Johannes},
  title     = {Large-scale literature mining to assess the relation between anti-cancer drugs and cancer types},
  series = {Journal of translational medicine},
  volume    = {19},
  journal   = {Journal of translational medicine},
  number    = {1},
  publisher = {BioMed Central},
  address   = {London},
  issn      = {1479-5876},
  doi       = {10.1186/s12967-021-02941-z},
  pages     = {13},
  year      = {2021},
  abstract  = {Background: There is a huge body of scientific literature describing the relation between tumor types and anti-cancer drugs. The vast amount of scientific literature makes it impossible for researchers and physicians to extract all relevant information manually. Methods: In order to cope with the large amount of literature we applied an automated text mining approach to assess the relations between 30 most frequent cancer types and 270 anti-cancer drugs. We applied two different approaches, a classical text mining based on named entity recognition and an AI-based approach employing word embeddings. The consistency of literature mining results was validated with 3 independent methods: first, using data from FDA approvals, second, using experimentally measured IC-50 cell line data and third, using clinical patient survival data. Results: We demonstrated that the automated text mining was able to successfully assess the relation between cancer types and anti-cancer drugs. All validation methods showed a good correspondence between the results from literature mining and independent confirmatory approaches. The relation between most frequent cancer types and drugs employed for their treatment were visualized in a large heatmap. All results are accessible in an interactive web-based knowledge base using the following link: . Conclusions: Our approach is able to assess the relations between compounds and cancer types in an automated manner. Both, cancer types and compounds could be grouped into different clusters. Researchers can use the interactive knowledge base to inspect the presented results and follow their own research questions, for example the identification of novel indication areas for known drugs.},
  language  = {en}
}