CAREER:A Unified Architecture for Data Mining Large Biomedical Literature Databases

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The large number of documents in biomedical literature databases and the lack of formal structure in the natural-language narrative in those documents make the search and processing very difficult to many scientists involved in bioinformatics research. This CAREER project is investigating the efficiency and effectiveness of information retrieval procedures, the effectiveness and robustness of pattern learning methods for information extraction, and information overload in text mining simultaneously in a coherent and unified framework for biomedical literature data mining. The deliverables of this project are: (1) to develop a semantic-based query expansion method for large biomedical literature databases; (2) to design an automatic pattern generation and evaluation method from unlabeled text files based on mutual bootstrapping and dynamic programming; (3) to develop a set of novel text mining algorithms such as ontology-enhanced textual clustering and text summarization. This project is testing its application in real-world bioinformatics domains such as chromatin interaction networks and microarray data analysis. The broad impact on society made by this project is the generation of a novel unified architecture for biomedical literature data mining. This integrated and complementary approach in a unified architecture has the potential to create a very powerful novel tool for bioinformatics and for most text processing tasks. This project has the potential to attract diverse collaborators who have an interest in accessing complex biomedical or general scientific data and information. Students are involved in this research through hands-on projects, a Co-Op program and courses at both the graduate and undergraduate level.