目的建立不同产地喜树果中9个成分含量同步检测方法,筛选影响其质量的差异标志物,并对其进行质量评价。方法对7省18个批次喜树果样品进行回流提取,提取物采用高效液相色谱法检测;采用正交偏最小二乘判别分析(Orthogonal partial least s...目的建立不同产地喜树果中9个成分含量同步检测方法,筛选影响其质量的差异标志物,并对其进行质量评价。方法对7省18个批次喜树果样品进行回流提取,提取物采用高效液相色谱法检测;采用正交偏最小二乘判别分析(Orthogonal partial least squares discriminant analysis,OPLS-DA)和加权逼近理想解排序(Technique for order preference by similarity to an ideal solution,TOPSIS)法建立喜树果质量优劣评价模型,对其质量差异性进行综合评价。结果3,4′-O-二甲基鞣花酸、丁香酸、10-羟基喜树碱、喜树碱、10-甲氧基喜树碱、三叶豆苷、短小蛇根草苷、金丝桃苷和喜果苷分别在0.51-12.75、0.23-5.75、3.21-80.25、4.45-111.25、1.88-47.00、0.41-10.25、2.05-51.25、0.34-8.50和7.95-198.75μg·mL^(-1)范围内线性关系良好(r>0.999),平均加样回收率96.95%-100.06%(RSD<2.0%);18批样品聚为3类;喜果苷、10-羟基喜树碱、喜树碱、10-甲氧基喜树碱和短小蛇根草苷可能是影响喜树果产品质量主要潜在标志物;加权TOPSIS法分析结果显示18批喜树果质量评价贴近度(Jb)在0.1090-0.7385,其中S14最大(0.7385)。结论建立了同时测定喜树果中9种成分含量的方法,操作简便、结果准确;采用OPLS-DA及加权TOPSIS法进行客观全面评价,可用于喜树果质量差异性评价。展开更多
Lung cancer is among the most frequent cancers in the world,with over one million deaths per year.Classification is required for lung cancer diagnosis and therapy to be effective,accurate,and reliable.Gene expression ...Lung cancer is among the most frequent cancers in the world,with over one million deaths per year.Classification is required for lung cancer diagnosis and therapy to be effective,accurate,and reliable.Gene expression microarrays have made it possible to find genetic biomarkers for cancer diagnosis and prediction in a high-throughput manner.Machine Learning(ML)has been widely used to diagnose and classify lung cancer where the performance of ML methods is evaluated to identify the appropriate technique.Identifying and selecting the gene expression patterns can help in lung cancer diagnoses and classification.Normally,microarrays include several genes and may cause confusion or false prediction.Therefore,the Arithmetic Optimization Algorithm(AOA)is used to identify the optimal gene subset to reduce the number of selected genes.Which can allow the classifiers to yield the best performance for lung cancer classification.In addition,we proposed a modified version of AOA which can work effectively on the high dimensional dataset.In the modified AOA,the features are ranked by their weights and are used to initialize the AOA population.The exploitation process of AOA is then enhanced by developing a local search algorithm based on two neighborhood strategies.Finally,the efficiency of the proposed methods was evaluated on gene expression datasets related to Lung cancer using stratified 4-fold cross-validation.The method’s efficacy in selecting the optimal gene subset is underscored by its ability to maintain feature proportions between 10%to 25%.Moreover,the approach significantly enhances lung cancer prediction accuracy.For instance,Lung_Harvard1 achieved an accuracy of 97.5%,Lung_Harvard2 and Lung_Michigan datasets both achieved 100%,Lung_Adenocarcinoma obtained an accuracy of 88.2%,and Lung_Ontario achieved an accuracy of 87.5%.In conclusion,the results indicate the potential promise of the proposed modified AOA approach in classifying microarray cancer data.展开更多
文摘目的建立不同产地喜树果中9个成分含量同步检测方法,筛选影响其质量的差异标志物,并对其进行质量评价。方法对7省18个批次喜树果样品进行回流提取,提取物采用高效液相色谱法检测;采用正交偏最小二乘判别分析(Orthogonal partial least squares discriminant analysis,OPLS-DA)和加权逼近理想解排序(Technique for order preference by similarity to an ideal solution,TOPSIS)法建立喜树果质量优劣评价模型,对其质量差异性进行综合评价。结果3,4′-O-二甲基鞣花酸、丁香酸、10-羟基喜树碱、喜树碱、10-甲氧基喜树碱、三叶豆苷、短小蛇根草苷、金丝桃苷和喜果苷分别在0.51-12.75、0.23-5.75、3.21-80.25、4.45-111.25、1.88-47.00、0.41-10.25、2.05-51.25、0.34-8.50和7.95-198.75μg·mL^(-1)范围内线性关系良好(r>0.999),平均加样回收率96.95%-100.06%(RSD<2.0%);18批样品聚为3类;喜果苷、10-羟基喜树碱、喜树碱、10-甲氧基喜树碱和短小蛇根草苷可能是影响喜树果产品质量主要潜在标志物;加权TOPSIS法分析结果显示18批喜树果质量评价贴近度(Jb)在0.1090-0.7385,其中S14最大(0.7385)。结论建立了同时测定喜树果中9种成分含量的方法,操作简便、结果准确;采用OPLS-DA及加权TOPSIS法进行客观全面评价,可用于喜树果质量差异性评价。
基金supported by the Deanship of Scientific Research,at Imam Abdulrahman Bin Faisal University.Grant Number:2019-416-ASCS.
文摘Lung cancer is among the most frequent cancers in the world,with over one million deaths per year.Classification is required for lung cancer diagnosis and therapy to be effective,accurate,and reliable.Gene expression microarrays have made it possible to find genetic biomarkers for cancer diagnosis and prediction in a high-throughput manner.Machine Learning(ML)has been widely used to diagnose and classify lung cancer where the performance of ML methods is evaluated to identify the appropriate technique.Identifying and selecting the gene expression patterns can help in lung cancer diagnoses and classification.Normally,microarrays include several genes and may cause confusion or false prediction.Therefore,the Arithmetic Optimization Algorithm(AOA)is used to identify the optimal gene subset to reduce the number of selected genes.Which can allow the classifiers to yield the best performance for lung cancer classification.In addition,we proposed a modified version of AOA which can work effectively on the high dimensional dataset.In the modified AOA,the features are ranked by their weights and are used to initialize the AOA population.The exploitation process of AOA is then enhanced by developing a local search algorithm based on two neighborhood strategies.Finally,the efficiency of the proposed methods was evaluated on gene expression datasets related to Lung cancer using stratified 4-fold cross-validation.The method’s efficacy in selecting the optimal gene subset is underscored by its ability to maintain feature proportions between 10%to 25%.Moreover,the approach significantly enhances lung cancer prediction accuracy.For instance,Lung_Harvard1 achieved an accuracy of 97.5%,Lung_Harvard2 and Lung_Michigan datasets both achieved 100%,Lung_Adenocarcinoma obtained an accuracy of 88.2%,and Lung_Ontario achieved an accuracy of 87.5%.In conclusion,the results indicate the potential promise of the proposed modified AOA approach in classifying microarray cancer data.
