Total 200 properties related to structural characteristics were employed to represent structures of 400 HA coded proteins of influenza virus as training samples. Some recognition models for HA proteins of avian influe...Total 200 properties related to structural characteristics were employed to represent structures of 400 HA coded proteins of influenza virus as training samples. Some recognition models for HA proteins of avian influenza virus (AIV) were developed using support vector machine (SVM) and linear discriminant analysis (LDA). The results obtained from LDA are as follows: the identification accuracy (Ria) for training samples is 99.8% and Ria by leave one out cross validation is 99.5%. Both Ria of 99.8% for training samples and Ria of 99.3% by leave one out cross validation are obtained using SVM model, respectively. External 200 HA proteins of influenza virus were used to validate the external predictive power of the resulting model. The external Ria for them is 95.5% by LDA and 96.5% by SVM, respectively, which shows that HA proteins of AIVs are preferably recognized by SVM and LDA, and the performances by SVM are superior to those by LDA.展开更多
基金National Natural Science Foundation of China(31700826)Chongqing Research Program of Basic Research and Frontier Technology(cstc2017jcyj AX0284)Visiting Scholar Foundation of Key Laboratory of Biorheological Science and Technology(Chongqing University),Ministry of Education(CQKLBST-2016-001)。
基金Foundations of National High Technology (863) Programme (Grant No. 2006AA02Z312)Innovative Group Programme for Graduates of Chongqing Uni-versity, Science and Innovation Fund (Grant No. 200711C1A0010260)+4 种基金National 111 Programme Introducing Talents of Discipline to Universities (Grant No. 0507111106)Chongqing Municipality Basic and Applied Fundamental Science Fund (Grant No. 01-3-6)National Chunhui Project Foundation (Grant No. 99-4-4+3-7)State Key Laboratory of Chemo/Biosensing and Chemometrics Fund (Grant No.2005012)Fok-Yingtung Educational Foundation (Grant No. 98-7-6)
文摘Total 200 properties related to structural characteristics were employed to represent structures of 400 HA coded proteins of influenza virus as training samples. Some recognition models for HA proteins of avian influenza virus (AIV) were developed using support vector machine (SVM) and linear discriminant analysis (LDA). The results obtained from LDA are as follows: the identification accuracy (Ria) for training samples is 99.8% and Ria by leave one out cross validation is 99.5%. Both Ria of 99.8% for training samples and Ria of 99.3% by leave one out cross validation are obtained using SVM model, respectively. External 200 HA proteins of influenza virus were used to validate the external predictive power of the resulting model. The external Ria for them is 95.5% by LDA and 96.5% by SVM, respectively, which shows that HA proteins of AIVs are preferably recognized by SVM and LDA, and the performances by SVM are superior to those by LDA.
