Extracting Molecular Entities and Their Interactions from Pathway Figures Based on Deep Learning Many new molecular mechanisms of genomics, pharmacogenomics, immunology, and other fields are reflected in pathway figures and need to be curated for various applications, especially in precision medicine. Current manual curation approaches are inadequate in keeping up with the pace of biomedical literature growth. Compared with textual representations, pathway figures in the biomedical literature often contain more direct representations of the mechanisms. However, no systematic method for curating pathway figures exists in publications due to date. Here, we propose a pathway curation pipeline, which integrates a deep learning model with an optical character recognition method and an image processing strategy to capture the locations, names, and interactions of pathway entities in the figure. Our pipeline was evaluated on the figures from PubMed publications. The results demonstrate that our model can effectively retrieve molecular entities and their interactions from pathway figures at a large scale. The proposed pipeline provides a complementary approach to textmining in biological literature mining. In future work, we will combine our method with textmining tools to enrich extracted information and reconstruct pathway mechanisms more precisely.

DrugDrug Interaction Extraction Using a Spanbased Neural Network Model Drugdrug interaction (DDI) extraction is one of important tasks in biomedical text mining. Compared with traditional pipeline extraction, joint methods enjoy lower error propagation, achieving better performances. However, biomedical texts contain a large number of overlapped relations, and existing joint extraction models fail to capture these entity relations. In this paper, we propose a novel joint model for DDI extraction. Different from the existing sequential models, our spanbased neural model treats the task as a relation triplets prediction problem, and builds the entitygraph by enumerating all possible candidate spans, and simultaneously models the relation between the correlated entities. Experimental results show that the proposed method significantly outperforms the strong baselines and previous methods, achieving the stateoftheart performances.