TY  - JOUR
A1  - Bauer, Chris
A1  - Herwig, Ralf
A1  - Lienhard, Matthias
A1  - Prasse, Paul
A1  - Scheffer, Tobias
A1  - Schuchhardt, Johannes
T1  - Large-scale literature mining to assess the relation between anti-cancer drugs and cancer types
JF  - Journal of translational medicine
N2  - 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.
KW  - Literature mining
KW  - Anti-cancer drugs
KW  - Tumor types
KW  - Word embeddings
KW  - Database
Y1  - 2021
U6  - https://doi.org/10.1186/s12967-021-02941-z
SN  - 1479-5876
VL  - 19
IS  - 1
PB  - BioMed Central
CY  - London
ER  - 
TY  - JOUR
A1  - Kaba, Hani E. J.
A1  - Maier, Natalia
A1  - Schliebe-Ohler, Nicole
A1  - Mayer, Yvonne
A1  - Mueller, Peter P.
A1  - van den Heuvel, Joop
A1  - Schuchhardt, Johannes
A1  - Hanack, Katja
A1  - Bilitewski, Ursula
T1  - Identification of whole pathogenic cells by monoclonal antibodies generated against a specific peptide from an immunogenic cell wall protein
JF  - Journal of microbiological methods
N2  - 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.
KW  - Candida
KW  - Diagnostics
KW  - Flow cytometry
KW  - Peptides
KW  - Bgl2p
Y1  - 2015
U6  - https://doi.org/10.1016/j.mimet.2014.11.003
SN  - 0167-7012
SN  - 1872-8359
VL  - 108
SP  - 61
EP  - 69
PB  - Elsevier
CY  - Amsterdam
ER  - 
TY  - CHAP
A1  - Motaln, Helena
A1  - Schuchhardt, Johannes
A1  - Stec, Karol
A1  - Breznik, Barbara
A1  - Ulrich, Henning
A1  - Lah, Tamara T.
T1  - Paradoxical role of mesenchymal stem cells in the glioblastoma  microenvironment
T2  - Anticancer research : international journal of cancer research and treatment
Y1  - 2014
SN  - 0250-7005
SN  - 1791-7530
VL  - 34
IS  - 10
SP  - 6071
EP  - 6072
PB  - International Institute of Anticancer Research
CY  - Athens
ER  -