Multi-omics studies, which explore the interactions between multiple types of biological factors, have significant advantages over single-omics analysis for their ability to provide a more holistic view of biological processes, uncover the causal and functional mechanisms for complex diseases, and facilitate new discoveries in precision medicine. However, omics datasets often contain missing values, and in multi-omics study designs it is common for individuals to be represented for some omics layers but not all. Since most statistical analyses cannot be applied directly to the incomplete datasets, imputation is typically performed to infer the missing values. Integrative imputation techniques which make use of the correlations and shared information among multi-omics datasets are expected to outperform approaches that rely on single-omics information alone, resulting in more accurate results for the subsequent downstream analyses. In this review, we provide an overview of the currently available imputation methods for handling missing values in bioinformatics data with an emphasis on multi-omics imputation. In addition, we also provide a perspective on how deep learning methods might be developed for the integrative imputation of multi-omics datasets.

多组学研究探索了多种生物因素之间的相互作用，与单组学分析相比具有显着优势，因为它们能够提供更全面的生物学过程视图，揭示复杂疾病的因果和功能机制，并促进新疾病的发展。精密医学的发现。但是，组学数据集通常包含缺失值，在多组学研究设计中，通常会在某些组学层中代表个人，但并不是全部。由于大多数统计分析无法直接应用于不完整的数据集，因此通常进行插补来推断缺失值。利用多组学数据集之间的相关性和共享信息的集成归因技术有望胜过仅依靠单组学信息的方法，从而为后续的下游分析提供更准确的结果。在这篇综述中，我们概述了用于处理生物信息学数据中缺失值的当前可用插补方法，重点是多组学插补。此外，我们还提供了有关如何开发深度学习方法以集成多组学数据集的观点。我们概述了用于处理生物信息学数据中缺失值的当前可用插补方法，重点是多组学插补。此外，我们还提供了有关如何开发深度学习方法以集成多组学数据集的观点。我们概述了用于处理生物信息学数据中缺失值的当前可用插补方法，重点是多组学插补。此外，我们还提供了有关如何开发深度学习方法以集成多组学数据集的观点。