Deep Learning‐Based Multiomics Data Integration Methods for Biomedical Application

Abstract

The innovation of high‐throughput technologies and medical radiomics allows biomedical data to accumulate at an astonishing rate. Several promising deep learning (DL) methods are developed to integrate multiomics data generated from a large number of samples. Herein, a comprehensive survey is conducted and the state‐of‐the‐art DL‐based multiomics data integration methods in the biomedical field are reviewed. These methods are classified into six categories according to their model framework, and the specific applicable scenarios of each category are summarized in five biomedicine aspects. DL‐based methods offer opportunities for disentangling biomolecular mechanisms in biomedical applications. There are, however, limitations with these methods, such as missing data problem and “black‐box” nature. A discussion of some of the recommendations for these challenges is ended.