abstract: Continuous advancements in biomedical research have resulted in the production of vast amounts of scientific data and literature discussing them. The ultimate goal of computational biology is to translate these large amounts of data into actual knowledge of the complex biological processes and accurate life science models. The ability to rapidly and effectively survey the literature is necessary for the creation of large scale models of the relationships among biomedical entities as well as hypothesis generation to guide biomedical research. To reduce the effort and time spent in performing these activities, an intelligent search system is required. Even though many systems aid in navigating through this wide collection of documents, the vastness and depth of this information overload can be overwhelming. An automated extraction system coupled with a cognitive search and navigation service over these document collections would not only save time and effort, but also facilitate discovery of the unknown information implicitly conveyed in the texts. This thesis presents the different approaches used for large scale biomedical named entity recognition, and the challenges faced in each. It also proposes BioEve: an integrative framework to fuse a faceted search with information extraction to provide a search service that addresses the user's desire for "completeness" of the query results, not just the top-ranked ones. This information extraction system enables discovery of important semantic relationships between entities such as genes, diseases, drugs, and cell lines and events from biomedical text on MEDLINE, which is the largest publicly available database of the world's biomedical journal literature. It is an innovative search and discovery service that makes it easier to search/navigate and discover knowledge hidden in life sciences literature. To demonstrate the utility of this system, this thesis also details a prototype enterprise quality search and discovery service that helps researchers with a guided step-by-step query refinement, by suggesting concepts enriched in intermediate results, and thereby facilitating the "discover more as you search" paradigm. / Dissertation/Thesis / M.S. Computer Science 2010
| Identifer | oai:union.ndltd.org:asu.edu/item:8601 |
| Date | January 2010 |
| Contributors | Kanwar, Pradeep (Author), Davulcu, Hasan (Advisor), Dinu, Valentin (Committee member), Li, Baoxin (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Masters Thesis |
| Format | 56 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/, All Rights Reserved |
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