BioPubMiner: machine learning component-based biomedical information analysis platform

Authors:
Jae-Hong Eom;Byoung-Tak Zhang
Affiliations:
Biointelligence Lab., School of Computer Science and Engineering, Seoul National University, Seoul, South Korea;Biointelligence Lab., School of Computer Science and Engineering, Seoul National University, Seoul, South Korea
Venue:
CIT'04 Proceedings of the 7th international conference on Intelligent Information Technology
Year:
2004

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Abstract

In this paper we introduce BioPubMiner, a machine learning component-based platform for biomedical information analysis. BioPubMiner 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 database to infer more interactions from the original interactions. 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.