  Effective biomedical literature retrieval (BLR) plays a central role in
precision medicine informatics. In this paper, we propose GRAPHENE, which is a
deep learning based framework for precise BLR. GRAPHENE consists of three main
different modules 1) graph-augmented document representation learning; 2) query
expansion and representation learning and 3) learning to rank biomedical
articles. The graph-augmented document representation learning module
constructs a document-concept graph containing biomedical concept nodes and
document nodes so that global biomedical related concept from external
knowledge source can be captured, which is further connected to a BiLSTM so
both local and global topics can be explored. Query expansion and
representation learning module expands the query with abbreviations and
different names, and then builds a CNN-based model to convolve the expanded
query and obtain a vector representation for each query. Learning to rank
minimizes a ranking loss between biomedical articles with the query to learn
the retrieval function. Experimental results on applying our system to TREC
Precision Medicine track data are provided to demonstrate its effectiveness.
