Abstract
Automated extraction of information from biological literature promises to play an increasingly important role in text-based knowledge discovery processes. This is particularly true in regards to high throughput approaches such as microarrays and combining data from different sources in a systems biology approach. We have developed an integrated system that combines protein/gene name dictionaries, synonymy dictionaries, natural language processing, and pattern matching rules to extract and organize gene relationships from full text articles. In the first phase full text articles were collected from 20 peer-reviewed journals in the field of molecular biology and biomedicine over the last 5 years (1999-2003). The extracted relationships were organized in a database that included the unique PubMed ID and section id (abstract, introduction, materials and method, and results and discussion) to identify the source article and section from which concepts were extracted. The system architecture, its uniqueness and advantages are presented in this paper. It is hoped that the resulting knowledge base will assist in the understanding of gene lists generated from microarray experiments.
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Bremer, E.G., Natarajan, J., Zhang, Y., DeSesa, C., Hack, C.J., Dubitzky, W. (2004). Text Mining of Full Text Articles and Creation of a Knowledge Base for Analysis of Microarray Data. In: López, J.A., Benfenati, E., Dubitzky, W. (eds) Knowledge Exploration in Life Science Informatics. KELSI 2004. Lecture Notes in Computer Science(), vol 3303. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30478-4_8
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DOI: https://doi.org/10.1007/978-3-540-30478-4_8
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