WANG Guoli, LIU Yang, HE Hongxia, JI Anquan, ZHANG Wei, CAO Yang, SUN Qifan
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Objective To explore the optimal combination of miRNA markers and classification model for effectively distinguishing menstrual from peripheral blood so as to build up one piece of simple and fast automatic discriminant software. Methods 10 kinds of miRNAs (miR-451a, miR-205-5p, miR-203a-3p, miR-214-3p, miR-144-3p, miR-144-5p, miR-654-5p, miR-888-5p, miR-891a-5p, miR-124-3p) were analyzed through quantitative real-time PCR into their relative expression quantities from menstrual (104 pieces) and peripheral (136 pieces) blood samples. Seven algorithmic models (kernel density estimation, K-nearest neighbor, logistic regression, linear discriminant analysis, supportive vector machine, neural network, random forest) were used for data analysis so that both the optimal miRNA marker combinations and appropriate algorithmic models were selected. Consequently, the software was therewith to construct for automatically distinguishing menstrual from peripheral blood with better identification effect. Results Three miRNAs of miR-205-5p, miR-203a-3p and miR-214-3p were of greatest difference between menstrual and peripheral blood, hence coming forth the better/optimal combinations of one or two of them assembling with miR-144-5p. Among the optimal combinations, the recommended one of miR-144-5p, miR-203a-3p and miR-205-5p demonstrated most robust. The appropriate classification model was the kernel density estimation for all the seven algorithmic ones, with the logistic regression being followed. Conclusions The automatic discriminant software constructed in this study is of friendly interface, simple use, accurate and reliable server algorithm, suiting for assisting forensic calculation on menstrual and peripheral blood identification, therefore capable of effectively facilitating the forensic analysis of evidential materials and great value of promotion and application.