Dimensionality Reduction based Transfer Learning applied to Pharmacogenomics Databases

Recent years have observed a number of Pharmacogenomics databases being published that enable testing of various predictive modeling techniques for personalized therapy applications. However, the consistencies between the databases are usually limited in spite of having significant number of common cell lines and drugs. In this article, we consider the problem of whether we can use the model learned from one secondary database to improve the prediction for the other target database. We illustrate using two pharmacogenomics databases that representing the databases using common basis vectors can improve prediction performance as compared to the naive application of a model trained on one database to another. We also elucidate the robustness of using PCA based basis vectors for scenarios with low correlated input features.