LitMiner is a literature data mining tool that is based on the annotation of key terms in article abstracts followed by statistical co-citation analysis of annotated key terms in order to predict relationships. Key terms belonging to four different categories are used for the annotation process:
- Genes: Names of genes and gene products. Gene name recognition is based on Ensembl . Synonyms and aliases are resolved.
- Chemical Compounds: Names of chemical compounds and their respective aliases.
- Diseases and Phenotypes: Names of diseases and phenotypes
- Tissues and Organs: Names of tissues and organs
This paper examines how the adoption of a subject-specific library service has changed the way in which its users interact with a digital library. The LitMiner text-analysis application was developed to enable biologists to explore gene relationships in the published literature. The application features a suite of interfaces that enable users to search PubMed as well as local databases, to view document abstracts, to filter terms, to select gene name aliases, and to visualize the co-occurrences of genes in the literature. At each of these stages, LitMiner offers the functionality of a digital library. Documents that are accessible online are identified by an icon. Users can also order documents from their institutions library collection from within the application. In so doing, LitMiner aims to integrate digital library services into the research process of its users.