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Querying parse tree database of Medline text to synthesize user-specific biomolecular networks.

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Department of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287, USA.


Curated biological knowledge of interactions and pathways is largely available from various databases, and network synthesis is a popular method to gain insight into the data. However, such data from curated databases presents a single view of the knowledge to the biologists, and it may not be suitable to researchers' specific needs. On the other hand, Medline abstracts are publicly accessible and encode the necessary information to synthesize different kinds of biological networks. In this paper, we propose a new paradigm in synthesizing biomolecular networks by allowing biologists to create their own networks through queries to a specialized database of Medline abstracts. With this approach, users can specify precisely what kind of information they want in the resulting networks. We demonstrate the feasibility of our approach in the synthesis of gene-drug, gene-disease and protein-protein interaction networks. We show that our approach is capable of synthesizing these networks with high precision and even finds relations that have yet to be curated in public databases. In addition, we demonstrate a scenario of recovering a drug-related pathway using our approach.

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