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1)  Cellular fatty acids
ISSR聚类分析
2)  ISSR analysis
ISSR分析
1.
Introduction test and ISSR analysis of Pinus contorta in semi-dry region of Jilin Province,China;
吉林省半干旱区小干松引种栽培试验及抗旱种源的ISSR分析
2.
Method: Seven wild populations of Changium smyrnioides and one cultivated population of Chuanminshen violaceum were studied by ISSR analysis.
方法:对7个不同野生群体的明党参和1个栽培群体的川明参进行ISSR分析。
3.
5% SDS,adding chloroform can effectively inhibit the activity of DNase and remove the impurity of protein when the sample was grinded,and the DNA with good quality and high concentration can be extracted once by chloroform/isopentanol(24/1) analysis,which can meet the needs of ISSR analysis.
研磨时加氯仿可以有效抑制DNA酶的活性和去除蛋白质杂质;用氯仿/异戊醇(24/1)抽提1次可获得质量好、浓度高的DNA,可以满足ISSR分析的需要。
3)  ISSR
ISSR分析
1.
Genetic Diversity of a Cultured Population on Pinctada Martensii Dunker by ISSR Marker;
马氏珠母贝群体内遗传多样性的ISSR分析
2.
In this study, random amplified polymorphic DNA (RAPD) technique and ISSR technique were used to reveal the genetic diversity in the same species of four indigenous populations collected from different altitudes of Niupidujuan (Rhododendronchrysanthum), a species in danger, endemic to Northeast China.
首先对牛皮杜鹃基因组DNA的提取方法进行了改良,以获得高质量的基因组DNA,用于RAPD和ISSR分析。
4)  ISSR analysis
ISSR标记分析
5)  cluster analysis
聚类分析
1.
Application of Cluster Analysis in the Judgment of the Aluminum Electrolyzer's Status;
聚类分析在铝电解槽况判断中的应用
2.
Water quality and cluster analysis of Huaihe River Basin in 2004;
2004年淮河流域水质状况和聚类分析
3.
Failure analysis of drill stem based on support vector machine and cluster analysis theory;
基于支持向量机和聚类分析理论的钻具失效分析方法
6)  clustering analysis
聚类分析
1.
Fuzzy clustering analysis of body type in 2D non-contacted body measurement system;
二维非接触人体测量中体型的模糊聚类分析
2.
Study on ranking standard of enterprise occupational injury risk based on clustering analysis of SPSS;
基于SPSS聚类分析的企业职业伤害风险分级标准研究
3.
Study on eco-environment quality assessment based on factor and clustering analysis;
基于因子分析和聚类分析的生态环境质量评价
补充资料:非系统聚类分析
分子式:
CAS号:

性质: 又称非谱系聚类分析。先将各样本粗略分为K个初始类,计算各类形心的坐标,再计算每个样本到类形心的距离,重新将样本聚集到最近距离的类中。再重新计算接受和失去了样本后的各类的形心,再对每个样本进行归类。循此进行,直到每个样本都归到了它与其类形心最靠近的类中,聚类过程停止,最后形成K类。

说明:补充资料仅用于学习参考,请勿用于其它任何用途。
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