AnchorDrug: A system for drug-induced gene expression prediction in new contexts through active learning

Large pre-trained models have been extensively explored for numerous biomedical tasks. However, the diversity and complexity of biological systems often make zero-shot learning in a new context challenging. In many instances, the budget allows for the acquisition of a small number of labeled data through experiments for few-shot learning. Yet, the methodology for selecting the optimal set of samples for these experiments remains underexplored. In this work, we present an application focused on drug-induced gene expression prediction to demonstrate a data-driven approach for facilitating sample selection. We developed a system named AnchorDrug, which predicts drug-induced gene expression changes in new cell lines after fine-tuning with experimental data from a limited number of drugs. Initially, we built a pre-trained model with a large dataset of drug-induced gene expressions. We then adopted active learning to identify an optimal set of drugs (i.e. anchor drugs) for experiments, aiming to ensure that the experimental data used for subsequent fine-tuning would maximize model performance. Several acquisition functions are customized and incorporated into our pipeline. Compared with knowledge-based drug selection, our customized active learning methods proved more effective in selecting anchor drugs. A model trained using data from anchor drugs can even perform better than that trained using all available data in certain scenarios. We further provided insights into the reasons behind its superior performance. Our system is designed to mimic real-world scenarios, enabling its easy application to real biomedical research projects.