TransMiner: Mining transitive associations among biological objects from text

Vijay Narayanasamy, Snehasis Mukhopadhyay, Mathew Palakal, David A. Potter

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


Associations among biological objects such as genes, proteins, and drugs can be discovered automatically from the scientific literature. TransMiner is a system for finding associations among objects by mining the Medline database of the scientific literature. The direct associations among the objects are discovered based on the principle of co-occurrence in the form of an association graph. The principle of transitive closure is applied to the association graph to find potential transitive associations. The potential transitive associations that are indeed direct are discovered by iterative retrieval and mining of the Medline documents. Those associations that are not found explicitly in the entire Medline database are transitive associations and are the candidates for hypothesis generation. The transitive associations were ranked based on the sum of weight of terms that cooccur with both the objects. The direct and transitive associations are visualized using a graph visualization applet. TransMiner was tested by finding associations among 56 breast cancer genes and among 24 objects in the calpain signal transduction pathway. TransMiner was also used to rediscover associations between magnesium and migraine.

Original languageEnglish (US)
Pages (from-to)864-873
Number of pages10
JournalJournal of biomedical science
Issue number6
StatePublished - 2004


  • Association
  • Discovery
  • Graph
  • Hypotheses generation
  • Text mining
  • Transitive closure


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