Transfer Learning for Predicting Gene Regulatory Effects of Chemicals

Abstract

Among the recent developments in bioinformatics and chemogenomics, various deep learning methods have been the most prevalent [4, 5, 9]. This resulted in an over-saturation of powerful models that easily pushed the limits of existing datasets. Subsequently, many novel advancements have been done with improvements to the datasets. Amidst these advancements, researchers of Deep Compound Profiler (DeepCOP) [10] set themselves apart with a novel method of introducing new features whilst keeping the deep learning model relatively basic. In this study, we propose to take this novel method one step further by applying transfer learning between cell lines. In order to better evaluate the benefits of transfer learning, we’ve introduced 2 drug-based data splits. The transfer learning method, as its core, utilizes the learned knowledge of “source” cell lines to give a head start to “target” cell lines. Taking advantage of prior knowledge from source cell lines not only boosts existing compounds’ effect prediction, but it can also be used as a premonition for the compounds’ (explicit to the source), effects on target cell lines before they are tested in real life. Our experiments showed improvements up to 22.81% improvement on area under ROC curve (AUC) on the split closest to a wet lab experiment.