As missing values are frequently present in genomic data, practical methods to handle missing data are necessary for downstream analyses that require complete data sets. State-of-the-art imputation techniques, including methods based on singular value decomposition and K-nearest neighbors, can be computationally expensive for large data sets and it is difficult to modify these algorithms to handle certain cases not missing at random.

中文翻译：

---

使用变分自动编码器进行基因组数据插补。

由于基因组数据中经常存在缺失值，因此对于需要完整数据集的下游分析来说，处理缺失数据的实用方法是必要的。最先进的插补技术，包括基于奇异值分解和 K 最近邻的方法，对于大型数据集来说计算成本可能很高，并且很难修改这些算法来处理某些非随机丢失的情况。