Concept Based Knowledge Discovery From Biomedical Literature
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Date
2009
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
University of the Western Cape
Abstract
Advancement in biomedical research and continuous growth of scientific literature available in electronic form, calls for innovative methods and tools for information management, knowledge discovery, and data integration. Many biomedical fields such as genomics, proteomics, metabolomics, genetics, and emerging disciplines like systems biology and conceptual biology require synergy between experimental, computational, data mining and text mining technologies. A large amount of biomedical information available in various repositories, such as the US National Library of Medicine Bibliographic Database, emerge as a potential source of textual data for knowledge discovery. Text mining and its application of natural language processing and machine learning technologies to problems of knowledge discovery, is one of the most challenging fields in bioinformatics. This thesis describes and introduces novel methods for knowledge discovery and presents a software system that is able to extract information from biomedical literature, review interesting connections between various biomedical concepts and in so doing, generates new hypotheses. The experimental results obtained by using methods described in this thesis, are
compared to currently published results obtained by other methods and a number of case studies are described. This thesis shows how the technology presented can be integrated with the researchers' own knowledge, experimentation and observations for optimal progression of scientific research.
Description
Philosophiae Doctor - PhD
Keywords
Bioinformaties, Text mining, PubMed, Entity recognition, Information extraction, Relation Extraction, Levenshtein distance, Supervised classification, Natural Language Processing, Machine learning