Discovering gene–gene relations from sequential sentence patterns in biomedical literature

In this paper, we have developed a gene–gene relation browser (DiGG) that integrates sequential pattern-mining and information-extraction model to extract from biomedical literature knowledge on gene–gene interactions. DiGG combines efficient mining technique to enable the discovery of frequent gene–gene sequences even for very long sentences. Our approach aims to detect associated gene relations that are often discussed in documents. Integration of the related relations will lead to an individual gene relation network. Graphic presentation will be used to demonstrate the relationships between gene products. A salient feature of this approach is that it incrementally outputs new frequent gene relations in an online visualization fashion.