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Genomics Inform > Volume 2(2); 2004 > Article
PubMiner: Machine Learning-based Text Mining for Biomedical Information Analysis.
Jae Hong Eom, Byoung Tak Zhang
Department of Biointelligence Laboratory, School of Computer Science and Engineering, Seoul National University, Seoul 151-744,Korea. btzhang@bi.snu.ac.kr
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 protein-protein 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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