Department of Computer Engineering, Islamic Azad University, Mashhad Branch, Mashhad, Iran.
Department of Electrical Engineering, University of Neyshabur, Neyshabur, Iran.
OMICS. 2023 Apr;27(4):141-152. doi: 10.1089/omi.2022.0155.
Omics data are multidimensional, heterogeneous, and high throughput. Robust computational methods and machine learning (ML)-based models offer new prospects to accelerate the data-to-knowledge trajectory. Deep learning (DL) is a powerful subset of ML inspired by brain structure and has created unprecedented momentum in bioinformatics and computational biology research. This article provides an overview of the current DL models applied to multi-omics data for both the beginner and the expert user. Additionally, COVID-19 will continue to impact planetary health as a pandemic and an endemic disease, with genomic and multi-omic pathophysiology. DL offers, therefore, new ways of harnessing systems biology research on COVID-19 diagnostics and therapeutics. Herein, we discuss, first, the statistical ML algorithms and essential deep architectures. Then, we review DL applications in multi-omics data analysis and their intersection with COVID-19. Finally, challenges and several promising directions are highlighted going forward in the current era of COVID-19.
组学数据具有多维性、异质性和高通量的特点。强大的计算方法和基于机器学习（ML）的模型为加速数据到知识的转化提供了新的前景。深度学习（DL）是受大脑结构启发的 ML 的一个强大子集，它在生物信息学和计算生物学研究中创造了前所未有的势头。本文为初学者和专家用户提供了一个应用于多组学数据的当前 DL 模型概述。此外，COVID-19 将继续作为一种大流行和地方病对行星健康产生影响，具有基因组和多组学生理病理学。因此，DL 为 COVID-19 诊断和治疗的系统生物学研究提供了新的途径。在此，我们首先讨论了统计 ML 算法和基本的深度学习架构。然后，我们回顾了 DL 在多组学数据分析中的应用及其与 COVID-19 的交集。最后，强调了在当前 COVID-19 时代面临的挑战和几个有前途的方向。
