A key aspect of machine learning-based relationship extraction algorithms is the availability of training data. Manually annotated corpora are valuable resources for this task, but the time and expertise required for their development explain that still few corpora are available. For tasks related to precision medicine, most of them are rather small (i.e., hundreds of sentences) or focus on specialized relationships (e.g., drug-drug interactions) that rarely fit what one wants to extract. In this paper, we experiment Tree-LSTM, to extract relationships from biomedical texts with high performance. In addition we show that relatively large corpora, even when focusing on a distinct type of relationships, can be use to improve the performance of deep learning-based system for relationship extraction tasks for which initial resources are scarce.