TIN-X is an interactive visualization tool for discovering interesting associations between diseases and potential drug targets.

We used natural language processing to identify disease and protein mentions in the text of PubMed abstracts. Using this data, we derived two metrics: novelty and importance. Novelty measures the relative scarcity of specific publications about a given concept (such as a target or a disease), while importance measures the relative strength of the association between two concepts. We then built this web tool, which enables users to explore the relationships between the novelty of potential drug targets and their importance to diseases.

Our approach was guided by the following assumptions:

• A target that is mentioned in many abstracts that also mention a specific disease is likely to be of importance to that disease.
• A target or disease that is mentioned in fewer abstracts is more novel and less well understood.
• Abstracts which mention only a few targets and diseases are more specific and should be given greater weight than those which mention many.

For more details about how these scores are computed, please see the paper cited below.