| Summary: | 碩士 === 國立中央大學 === 資訊工程學系 === 104 === Nowadays, understanding pathway is one of the main purpose of biomedical domains, because the biological pathway involves various regulation mechanisms. Many regulation mechanisms have being discovered and presented in biomedical literature, allowing life scientists to perceive the latest results. It also has being highly demanded within the scientific community in the text mining for biomedical researches. Biological Expression Language (BEL) is designed to capture relationships between the two biological entities, such as gene, protein and chemical in scientific literatures. This is can not only describe the positive/negative relationship between biomedical entities, but represent biomedical function-level information, such as complex abundance, chaperone protein, catalyst and so on. In related research, the latest performance of function-level classification is 30.5\%, and the performance will effect on the BEL full-statement performance. In order to enhance the integrity of the BEL full-statements, we proposed a K-nearest neighbor (KNN) approach inspired by Principal Component Analysis (PCA) to recognize the function-level terms automatically. In experimental results, combination of PCA and KNN has the higher performance than SVM-based method, and it can achieve F-score of 59.70\%. In conclusion, we hope that the higher performance of function-level classification can not only enhance the integrity of BEL full-statement, but help to construct complete biological networks and to accelerate the biomedical research processes for life scientists.
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