A study was performed to determine how bioinformatics algorithms could be employed to classify and estimate 50 potato commercial and wild populations using microsatellite (SSR) markers. In this study, 40 SSR primers were used for estimating the genetic structure of all 50 potato populations. The data were generated in two different experiments including 32 wild and 18 commercial accessions. The results indicate that 31 SSR primers were polymorphic, and also these primers with 334 alleles were analyzed. Analyses through data cleaning, attribute weighting, and machine learning classified the populations into different categories. The machine learning methods used here for classifying the potato accessions discovered that the SS110 was the best SSR primer for the potato population analysis, and also, it can be emphasized that the machine learning analyses are the best tool for the classification of potato populations based on populations specific genetic architecture.

中文翻译：

---

使用SSR标记检测马铃薯种群的机器学习

进行了一项研究，以确定如何利用生物信息学算法使用微卫星（SSR）标记对50个马铃薯商业种群和野生种群进行分类和估计。在这项研究中，使用了40个SSR引物来估计所有50个马铃薯种群的遗传结构。数据是在两个不同的实验中生成的，包括32个野生和18个商业登录。结果表明31个SSR引物具有多态性，并分析了这些引物与334个等位基因。通过数据清理，属性加权和机器学习进行的分析将人口分为不同的类别。此处用于对马铃薯种质进行分类的机器学习方法发现，SS110是用于马铃薯种群分析的最佳SSR引物，而且，