Analysis of Protein/Protein Interactions Through Biomedical Literature: Text Mining of Abstracts vs. Text Mining of Full Text Articles

The challenge of knowledge management in the pharmaceutical industry is twofold. First it has to address the integration of sequence data with the vast and growing body of data from functional analysis of genes with the information in huge historical archival databases. Second, as the number of biomedical publications exponentially increases (Medline now contains more than 13 million records), researchers require assistance in order to broaden their vision and comprehension of scientific domains. Analogous to data mining in the sense that it uncovers relationships in information, text mining uncovers relationships in a text collection and leverages the creativity of the knowledge worker in the exploration of these relationships and in the discovery of new knowledge. We describe herein a text mining method to automatically detect protein interactions which are described across a large amount of scientific publications. This method relies on natural language processing to identify protein names, their synonyms and the various interactions they can bear with other proteins. We have then compared text mining analysis on abstracts to the same kind of analysis on full text articles to assess how much information is lost when only abstracts are processed. Our results show that: 1)LexiQuest Mine is a very versatile and accurate tool when mining biomedical literature to analyze interactions between proteins. 2)Mining only abstracts can be sufficient and time saving for applications that do not require a high level of detail on a large scale whereas mining full text articles is to be chosen for more exhaustive applications designed to address a specific issue. Availability: LexiQuest Mine is available for commercial licensing from SPSS, Inc.

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