PubMiner: Machine Learning-Based Text Mining System for Biomedical Information Mining
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- 作者：Jae-Hong Eom ; Byoung-Tak Zhang
- 关键词：Natural Language Processing ; Data Mining ; Machine Learning ; Bioinformatics ; Software Application
- 刊名：Lecture Notes in Computer Science
- 出版时间：2004
- 出版年：2004
- 期刊代码：99_0302-9743
- 类别：cp
- 卷：3192
- 期：1
- 页码：216-225
- 数据来源：sp
摘要
PubMiner, an intelligent machine learning based text mining system for mining biological information from the literature is introduced. PubMiner utilize natural language processing and machine learning based data mining techniques for mining useful biological information such as protein-protein interaction from the massive literature data. The system recognizes biological terms such as gene, protein, and enzymes and extracts their interactions described in the document through natural language analysis. The extracted interactions are further analyzed with a set of features of each entity which were constructed 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 evaluation of system performance proceeded with the protein interaction data of S.cerevisiae (bakers yeast) from MIPS and SGD. Keywords: Natural Language Processing, Data Mining, Machine Learning, Bioinformatics, and Software Application