Characterizing Cancer Information Systems
Journal of Medical Systems
Mining scientific literature to predict new relationships
Intelligent Data Analysis
Journal of Biomedical Informatics
Computer Methods and Programs in Biomedicine
A new evaluation methodology for literature-based discovery systems
Journal of Biomedical Informatics
Mining connections between chemicals, proteins, and diseases extracted from Medline annotations
Journal of Biomedical Informatics
A text-mining technique for extracting gene-disease associations from the biomedical literature
International Journal of Bioinformatics Research and Applications
Mining medline for new possible relations of concepts
CIS'04 Proceedings of the First international conference on Computational and Information Science
Investigation of the changes of temporal topic profiles in biomedical literature
KDLL'06 Proceedings of the 2006 international conference on Knowledge Discovery in Life Science Literature
Towards an automated analysis of biomedical abstracts
DILS'06 Proceedings of the Third international conference on Data Integration in the Life Sciences
The arrowsmith project: 2005 status report
DS'05 Proceedings of the 8th international conference on Discovery Science
Literature-based discovery: Beyond the ABCs
Journal of the American Society for Information Science and Technology
Discovering discovery patterns with predication-based Semantic Indexing
Journal of Biomedical Informatics
Automatic retrieval of current evidence to support update of bibliography in clinical guidelines
Expert Systems with Applications: An International Journal
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Motivation: New relationships are often implicit from existing information, but the amount and growth of published literature limits the scope of analysis an individual can accomplish. Our goal was to develop and test a computational method to identify relationships within scientific reports, such that large sets of relationships between unrelated items could be sought out and statistically ranked for their potential relevance as a set. Results: We first construct a network of tentative relationships between 'objects' of biomedical research interest (e.g. genes, diseases, phenotypes, chemicals) by identifying their co-occurrences within all electronically available MEDLINE records. Relationships shared by two unrelated objects are then ranked against a random network model to estimate the statistical significance of any given grouping. When compared against known relationships, we find that this ranking correlates with both the probability and frequency of object co-occurrence, demonstrating the method is well suited to discover novel relationships based upon existing shared relationships. To test this, we identified compounds whose shared relationships predicted they might affect the development and/or progression of cardiac hypertrophy. When laboratory tests were performed in a rodent model, chlorpromazine was found to reduce the progression of cardiac hypertrophy. Supplementary information: http://innovation.swmed.edu/IRIDESCENT/Supplemental_Info.htm