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  -