TY  - JOUR
A1  - Prasse, Paul
A1  - Iversen, Pascal
A1  - Lienhard, Matthias
A1  - Thedinga, Kristina
A1  - Herwig, Ralf
A1  - Scheffer, Tobias
T1  - Pre-Training on In Vitro and Fine-Tuning on Patient-Derived Data Improves Deep Neural Networks for Anti-Cancer Drug-Sensitivity Prediction
JF  - MDPI
N2  - Large-scale databases that report the inhibitory capacities of many combinations of candidate drug compounds and cultivated cancer cell lines have driven the development of preclinical drug-sensitivity models based on machine learning. However, cultivated cell lines have devolved from human cancer cells over years or even decades under selective pressure in culture conditions. Moreover, models that have been trained on in vitro data cannot account for interactions with other types of cells. Drug-response data that are based on patient-derived cell cultures, xenografts, and organoids, on the other hand, are not available in the quantities that are needed to train high-capacity machine-learning models. We found that pre-training deep neural network models of drug sensitivity on in vitro drug-sensitivity databases before fine-tuning the model parameters on patient-derived data improves the models’ accuracy and improves the biological plausibility of the features, compared to training only on patient-derived data. From our experiments, we can conclude that pre-trained models outperform models that have been trained on the target domains in the vast majority of cases.
KW  - deep neural networks
KW  - drug-sensitivity prediction
KW  - anti-cancer drugs
Y1  - 2022
U6  - https://doi.org/10.3390/cancers14163950
SN  - 2072-6694
VL  - 14
SP  - 1
EP  - 14
PB  - MDPI
CY  - Basel, Schweiz
ET  - 16
ER  -