Allergenicity Investigation Through n-peptide Based Features on Integrated Machine Learning Methods

• Main Authors: SU.CHING-TING, 蘇郅挺
• Other Authors: YU,CHIN-SHENG
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/e3wxw3

碩士 === 逢甲大學 === 資訊工程學系 === 106 === An allergic reaction is an overreaction that our body's immune system misinterprets some otherwise harmless substances as a threat to our body. The substances that can cause allergic reactions are called allergens. At present, studies on allergic proteins are almost always based on predictions. Data sets are created using known allergen proteins and non-allergenic proteins. After feature extraction, prediction models are established through machine learning methods, followed by unknown proteins. Sequences can be classified using the previously constructed predictive model. This paper builds on the future analysis of the forecast results. For further research, we use the SVM (Support Vector Machine) to integrate the first-level forecast results into the second-level forecast model. The predicted results are as follows (test set results SE = 70.9, ACC = 96.2%, SP = 99.1%, PR = 90%, MCC = 0.78). (Independent test set results SE = 73.0%, ACC = 96.4%, SP = 99.1%, PR = 90.3%, MCC = 0.79) Based on the results of this prediction, we analyzed the allergen sequence and returned the final predicted result to the original protein sequence, and we hope to obtain the analysis results related to the criticality of the allergen protein.