Computational Methods for Intelligent Information Access
Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Concept extraction and association from cancer literature
Proceedings of the 4th international workshop on Web information and data management
Extracting molecular binding relationships from biomedical text
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
A Probabilistic Model for Identifying Protein Names and their Name Boundaries
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Generating association graphs of non-cooccurring text objects using transitive methods
Proceedings of the 2005 ACM symposium on Applied computing
A hybrid approach to protein name identification in biomedical texts
Information Processing and Management: an International Journal
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Protein association discovery can directly contribute toward developing protein pathways; hence it is a significant problem in bioinformatics. LUCAS (Library of User-Oriented Concepts for Access Services) was designed to automatically extract and determine associations among proteins from biomedical literature. Such a tool has notable potential to automate database construction in biomedicine, instead of relying on experts' analysis. This paper reports on the mechanisms for automatically generating clusters of proteins. A formal evaluation of the system, based on a subset of 2000 MEDLINE titles and abstracts, has been conducted against Swiss-Prot database in which the associations among concepts are entered by experts manually.