Classifying Biomedical Literature Providing Protein Function Evidence

• Published in: ETRI journal 2015, 37(4), , pp.813-823
• Main Authors: Lim, Joon‐Ho, Lee, Kyu‐Chul
• Format: Article
• Language: English
• Subjects: automatic text summarization; document classification; Information retrieval; protein function evidence; 전자/정보통신공학
• ISSN: 1225-6463; 2233-7326
• DOI: 10.4218/etrij.15.0114.0041

Because protein is a primary element responsible for biological or biochemical roles in living bodies, protein function is the core and basis information for biomedical studies. However, recent advances in bio technologies have created an explosive increase in the amount of published literature; therefore, biomedical researchers have a hard time finding needed protein function information. In this paper, a classification system for biomedical literature providing protein function evidence is proposed. Note that, despite our best efforts, we have been unable to find previous studies on the proposed issue. To classify papers based on protein function evidence, we should consider whether the main claim of a paper is to assert a protein function. We, therefore, propose two novel features — protein and assertion. Our experimental results show a classification performance with 71.89% precision, 90.0% recall, and a 79.94% F‐measure. In addition, to verify the usefulness of the proposed classification system, two case study applications are investigated — information retrieval for protein function and automatic summarization for protein function text. It is shown that the proposed classification system can be successfully applied to these applications.