Department of Biostatistics, University of Florida, 2004 Mowry Rd, Gainesville, FL 32611, USA.
Department of Mathematics and Statistics, University of New Hampshire, 33 Academic Way, Durham, NH 03824, USA.
Brief Bioinform. 2024 Jul 25;25(5). doi: 10.1093/bib/bbae448.
With rapidly evolving high-throughput technologies and consistently decreasing costs, collecting multimodal omics data in large-scale studies has become feasible. Although studying multiomics provides a new comprehensive approach in understanding the complex biological mechanisms of human diseases, the high dimensionality of omics data and the complexity of the interactions among various omics levels in contributing to disease phenotypes present tremendous analytical challenges. There is a great need of novel analytical methods to address these challenges and to facilitate multiomics analyses. In this paper, we propose a multimodal functional deep learning (MFDL) method for the analysis of high-dimensional multiomics data. The MFDL method models the complex relationships between multiomics variants and disease phenotypes through the hierarchical structure of deep neural networks and handles high-dimensional omics data using the functional data analysis technique. Furthermore, MFDL leverages the structure of the multimodal model to capture interactions between different types of omics data. Through simulation studies and real-data applications, we demonstrate the advantages of MFDL in terms of prediction accuracy and its robustness to the high dimensionality and noise within the data.
随着高通量技术的快速发展和成本的持续降低，在大规模研究中收集多模态组学数据已成为可能。虽然研究多组学为理解人类疾病的复杂生物学机制提供了一种新的综合方法，但组学数据的高维性以及各种组学水平之间相互作用的复杂性对疾病表型的影响带来了巨大的分析挑战。需要新的分析方法来应对这些挑战，并促进多组学分析。在本文中，我们提出了一种用于分析高维多组学数据的多模态功能深度学习（MFDL）方法。MFDL 方法通过深度神经网络的层次结构来模拟多组学变异与疾病表型之间的复杂关系，并使用功能数据分析技术处理高维组学数据。此外，MFDL 利用多模态模型的结构来捕捉不同类型组学数据之间的相互作用。通过模拟研究和真实数据应用，我们证明了 MFDL 在预测准确性方面的优势，以及其对数据中的高维性和噪声的稳健性。
