Here we deposit the code and the models (weights) of the implementation of the framework "Two-step transfer learning improves deep learning-based drug response prediction in small datasets: A case study of glioblastoma". The goal of the study is to investigate how transfer learning (TL) alleviates the small sample size problem. A two-step TL framework was constructed for a difficult task: predicting the response of the drug temozolomide (TMZ) in glioblastoma (GBM) cell cultures.
Three publicly available datasets were used in the study.
GDSC dataset contains RNA expressions of 558-710 cell cultures from 20-32 tissue sites (including GBM) treated by TMZ, Bortezomib, Cyclophosphamide and Oxaliplatin, respectively.
HGCC contains RNA expressions of 83 GBM cell cultures treated by TMZ (preprocessed as shown in the R script).
GSE232173 contains RNA expressions of 22 GBM cell cultures treated by TMZ (preprocessed as shown in the R script).
The two-step TL consists of three parts. The python code and the weights extracted from each step of the DL models are deposit here.
First, the DL models were pre-trained on the cell cultures treated by each of the four drugs from GDSC, respectively. Refered as Experiment 1 in the manuscript.
Second, the DL models were refined on HGCC, where the best source drug was identified. Referred as Experiment 3 in the manuscript.
Finally, the DL model was validated on GSE232173. Referred as Experiment 7 in the manuscript.