Biomedical Text Mining: The biomedical literature databases continue to grow rapidly with vital information that is important for conducting sound biomedical research. BioMap is an attempt to create a scalable knowledgebase of biological relationships extracted from vast amount of biomedical literature data. The development of BioMap system addresses several innovative research issues related to knowledge discovery from literature documents and real-time, interactive access of this knowledge. Specific problems that are being investigated are: discovering explicit, implicit and directional relationships among biological entities from abstracts and full text documents; discovering both explicit and implicit protein-protein interactions and computationally validating these interactions; and, obtaining novel pathways associated with specific diseases in question. Protein-protein, gene-protein, disease-drug interactions are examples of biological associations that are automatically discovered from large number of literature documents. BioMap can discover interactions in user specified biomedical problem domains such as inflammatory diseases, regenerative biology, cancer, etc., and provide a user-centric view of the knowledge that are discovered.
Intelligent Information Management Systems: There is a critical need for innovative information management and knowledge discovery tools to sift through vast volumes of heterogeneous data from various information sources. This project looks into developing Intelligent Software Systems that can integrate information resources and extract embedded knowledge from these information sources.
He is currently working on problems related to information management in the field of life sciences.