Abdelaziz Esraa Hamdi, Ismail Rasha, Mabrouk Mai S, Amin Eman
Faculty of Computer and Information Sciences, Ainshams University, Cairo, Egypt.
Information Technology and Computer Science School, Nile University, Cairo, Egypt.
Comput Biol Chem. 2024 Dec;113:108254. doi: 10.1016/j.compbiolchem.2024.108254. Epub 2024 Oct 16.
Precision medicine has gained considerable popularity since the "one-size-fits-all" approach did not seem very effective or reflective of the complexity of the human body. Subsequently, since single-omics does not reflect the complexity of the human body's inner workings, it did not result in the expected advancement in the medical field. Therefore, the multi-omics approach has emerged. The multi-omics approach involves integrating data from different omics technologies, such as DNA sequencing, RNA sequencing, mass spectrometry, and others, using computational methods and then analyzing the integrated result for different downstream analysis applications such as survival analysis, cancer classification, or biomarker identification. Most of the recent reviews were constrained to discussing one aspect of the multi-omics analysis pipeline, such as the dimensionality reduction step, the integration methods, or the interpretability aspect; however, very few provide a comprehensive review of every step of the analysis. This study aims to give an overview of the multi-omics analysis pipeline, starting with the most popular multi-omics databases used in recent literature, dimensionality reduction techniques, details the different types of data integration techniques and their downstream analysis applications, describes the most commonly used evaluation metrics, highlights the importance of model interpretability, and lastly discusses the challenges and potential future work for multi-omics data integration in precision medicine.
由于“一刀切”的方法似乎不太有效，也无法反映人体的复杂性，精准医学已变得相当流行。随后，由于单一组学无法反映人体内部运作的复杂性，因此未能在医学领域带来预期的进展。于是，多组学方法应运而生。多组学方法包括使用计算方法整合来自不同组学技术的数据，如DNA测序、RNA测序、质谱分析等，然后针对生存分析、癌症分类或生物标志物识别等不同的下游分析应用对整合结果进行分析。最近的大多数综述都局限于讨论多组学分析流程的一个方面，如降维步骤、整合方法或可解释性方面；然而，很少有综述对分析的每个步骤进行全面回顾。本研究旨在概述多组学分析流程，首先介绍近期文献中使用的最流行的多组学数据库、降维技术，详细阐述不同类型的数据整合技术及其下游分析应用，描述最常用的评估指标，强调模型可解释性的重要性，最后讨论精准医学中多组学数据整合面临的挑战和潜在的未来工作。
