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PubMiner: Machine Learning-based Text Mining for Biomedical Information Analysis  

Eom, Jae-Hong (Biointelligence Laboratory, School of Computer Science and Engineering, Seoul National University)
Zhang, Byoung-Tak (Biointelligence Laboratory, School of Computer Science and Engineering, Seoul National University)
Publication Information
Genomics & Informatics / v.2, no.2, 2004 , pp. 99-106 More about this Journal
Abstract
In this paper we introduce PubMiner, an intelligent machine learning based text mining system for mining biological information from the literature. PubMiner employs natural language processing techniques and machine learning based data mining techniques for mining useful biological information such as protein­protein interaction from the massive literature. The system recognizes biological terms such as gene, protein, and enzymes and extracts their interactions described in the document through natural language processing. The extracted interactions are further analyzed with a set of features of each entity that were collected from the related public databases to infer more interactions from the original interactions. An inferred interaction from the interaction analysis and native interaction are provided to the user with the link of literature sources. The performance of entity and interaction extraction was tested with selected MEDLINE abstracts. The evaluation of inference proceeded using the protein interaction data of S. cerevisiae (bakers yeast) from MIPS and SGD.
Keywords
Biomedical Text Mining; Data Mining; Machine Learning; Software Application;
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