In this work, support vector classification (SVC) algorithm was used to build structure-activity relationship (SAR) model of the 5-hydroxytryptamine type 3 (5-HT3 ) receptor antagonists with 26 compounds. In a b...In this work, support vector classification (SVC) algorithm was used to build structure-activity relationship (SAR) model of the 5-hydroxytryptamine type 3 (5-HT3 ) receptor antagonists with 26 compounds. In a benchmark test, SVC was compared with several techniques of machine learning currently used in the field. The prediction performance of the model was discussed on the basis of the leave-one-out cross-validation. The results show that the accuracy of prediction of SVC model was higher than those of back propagation artificial neural network (BP ANN), K-nearest neighbor (KNN) and Fisher methods.展开更多
目的:采用Meta分析方法对阿瑞匹坦联合5-HT3受体拮抗剂和地塞米松预防化疗相关性恶心和呕吐进行系统评价。方法:检索Pubmed、EMbase、Cochrane Library、中国知网CNKI全文数据库、维普数据库、万方数据库和中国生物医学文献数据库,查找2...目的:采用Meta分析方法对阿瑞匹坦联合5-HT3受体拮抗剂和地塞米松预防化疗相关性恶心和呕吐进行系统评价。方法:检索Pubmed、EMbase、Cochrane Library、中国知网CNKI全文数据库、维普数据库、万方数据库和中国生物医学文献数据库,查找2003年1月至2013年12月公开发表的研究阿瑞匹坦联合5-HT3受体拮抗剂和地塞米松预防化疗相关性恶心和呕吐的临床随机对照试验。按照纳入与排除标准选择文献,质量评估,资料提取,采用Rev Man 5.2软件进行Meta分析。结果:共纳入12篇英文RCT文献,均为高质量研究。Meta分析结果显示,阿瑞匹坦联合5-HT3受体拮抗剂、地塞米松治疗(三联治疗)在预防高、中度致吐性化疗相关性恶心和呕吐的总体完全缓解率[OR=1.91,95%CI(1.68,2.17),P<0.00001]、急性完全缓解率[OR=1.89,95%CI(1.48,2.42),P<0.00001]、迟发性完全缓解率[OR=2.05,95%CI(1.68,2.51),P<0.00001]明显高于5-HT3受体拮抗剂、地塞米松治疗(二联治疗),两组差异有统计学意义。结论:阿瑞匹坦可以显著提高高、中度致吐性化疗的总体、急性和迟发性恶心和呕吐完全缓解率,尤其是在提高迟发性恶心和呕吐完全缓解率更为明显。展开更多
基金Project supported by National Natural Science Foundation of China( Grant No. 20373040)
文摘In this work, support vector classification (SVC) algorithm was used to build structure-activity relationship (SAR) model of the 5-hydroxytryptamine type 3 (5-HT3 ) receptor antagonists with 26 compounds. In a benchmark test, SVC was compared with several techniques of machine learning currently used in the field. The prediction performance of the model was discussed on the basis of the leave-one-out cross-validation. The results show that the accuracy of prediction of SVC model was higher than those of back propagation artificial neural network (BP ANN), K-nearest neighbor (KNN) and Fisher methods.
文摘目的:采用Meta分析方法对阿瑞匹坦联合5-HT3受体拮抗剂和地塞米松预防化疗相关性恶心和呕吐进行系统评价。方法:检索Pubmed、EMbase、Cochrane Library、中国知网CNKI全文数据库、维普数据库、万方数据库和中国生物医学文献数据库,查找2003年1月至2013年12月公开发表的研究阿瑞匹坦联合5-HT3受体拮抗剂和地塞米松预防化疗相关性恶心和呕吐的临床随机对照试验。按照纳入与排除标准选择文献,质量评估,资料提取,采用Rev Man 5.2软件进行Meta分析。结果:共纳入12篇英文RCT文献,均为高质量研究。Meta分析结果显示,阿瑞匹坦联合5-HT3受体拮抗剂、地塞米松治疗(三联治疗)在预防高、中度致吐性化疗相关性恶心和呕吐的总体完全缓解率[OR=1.91,95%CI(1.68,2.17),P<0.00001]、急性完全缓解率[OR=1.89,95%CI(1.48,2.42),P<0.00001]、迟发性完全缓解率[OR=2.05,95%CI(1.68,2.51),P<0.00001]明显高于5-HT3受体拮抗剂、地塞米松治疗(二联治疗),两组差异有统计学意义。结论:阿瑞匹坦可以显著提高高、中度致吐性化疗的总体、急性和迟发性恶心和呕吐完全缓解率,尤其是在提高迟发性恶心和呕吐完全缓解率更为明显。