Visualization and Language Processing for Supporting Analysis across the Biomedical Literature

Finding relevant publications in the large and rapidly growing body of biomedical literature is challenging. Search queries on PubMed often return thousands of publications and it can be a tedious task to filter out irrelevant publications and choose a manageable set to read. We have developed a visual analytics system, named Bio-Jigsaw, which acts like a visual index on a document collection and supports biologists in investigating and understanding connections between biological entities. We apply natural language processing techniques to identify biological entities such as genes and pathways and visualize connections among them via multiple representations. Connections are based on cooccurrence in abstracts and also are drawn from ontologies or annotations in digital libraries. We demonstrate how Bio-Jigsaw can be used to analyze a PubMed search query on a gene related to breast cancer resulting in over 1500 primary papers.