Abstract Background The rapid growth of deep learning, as well as the vast and ever-growing amount of available data, have provided ample opportunity for advances in fusion and analysis of complex and heterogeneous data types. Different data modalities provide complementary information that can be leveraged to gain a more complete understanding of each subject. In the biomedical domain, multi-omics data includes molecular (genomics, transcriptomics, proteomics, epigenomics, metabolomics, etc.) and imaging (radiomics, pathomics) modalities which, when combined, have the potential to improve performance on prediction, classification, clustering and other tasks. Deep learning encompasses a wide variety of methods, each of which have certain strengths and weaknesses for multi-omics integration. Method In this review, we categorize recent deep learning-based approaches by their basic architectures and discuss their unique capabilities in relation to one another. We also discuss some emerging themes advancing the field of multi-omics integration. Results Deep learning-based multi-omics integration methods were categorized broadly into non-generative (feedforward neural networks, graph convolutional neural networks, and autoencoders) and generative (variational methods, generative adversarial models, and a generative pretrained model). Generative methods have the advantage of being able to impose constraints on the shared representations to enforce certain properties or incorporate prior knowledge. They can also be used to generate or impute missing modalities. Recent advances achieved by these methods include the ability to handle incomplete data as well as going beyond the traditional molecular omics data types to integrate other modalities such as imaging data. Conclusion We expect to see further growth in methods that can handle missingness, as this is a common challenge in working with complex and heterogeneous data. Additionally, methods that integrate more data types are expected to improve performance on downstream tasks by capturing a comprehensive view of each sample.

Jenna L Ballard

Zexuan Wang

Wenrui Li

Li Shen

Qi Long

BioData Mining

BIODATA MINING 是一本开放获取、开放的同行评审期刊，涵盖应用于高维生物和生物医学数据的数据挖掘各个方面的研究，重点关注从大规模遗传、转录组、基因组、蛋白质组和代谢组学数据。主题领域包括但不限于：-新型数据挖掘和机器学习算法的开发、评估和应用。-传统数据挖掘和机器学习算法的适应、评估和应用。-开源软件用于数据挖掘和机器学习算法的应用。-数据库、软件和网络服务的设计、开发和集成，用于大规模研究数据的存储、管理、检索和分析。-预处理、后处理、数据挖掘和机器学习结果的建模和解释，用于生物解释和知识发现。

BioData Mining is an open-access, peer-reviewed journal that focuses on all aspects of data mining applied to high-dimensional biological and biomedical data. The journal covers topics including but not limited to: the development, evaluation, and application of novel data mining and machine learning algorithms; the adaptation, evaluation, and application of traditional data mining and machine learning algorithms; the application of open-source software for data mining and machine learning; the design, development, and integration of databases, software, and web services for the storage, management, retrieval, and analysis of large-scale research data; and the modeling and interpretation of preprocessing, postprocessing, data mining, and machine learning results for biological interpretation and knowledge discovery.

《BioData Mining》是一本开放获取的同行评审期刊，专注于高维生物和生物医学数据的数据挖掘研究。期刊涵盖的主题包括：新型数据挖掘和机器学习算法的开发、评估与应用；传统数据挖掘和机器学习算法的适应、评估与应用；开源软件在数据挖掘和机器学习中的应用；数据库、软件和网络服务的设计、开发与集成，以支持大规模研究数据的存储、管理、检索与分析；以及数据的预处理、后处理、挖掘和机器学习结果的建模与解释，以促进生物学解释和知识发现。

Category	Quartile
生物学	3
生物学, 数学与计算生物学	2

Deep learning-based approaches for multi-omics data integration and analysis

ivySCI AI Smartly Parses PDF, Answers Researchers' Questions, and Helps You Understand Papers in Seconds

Journal Info

Category	Quartile
MATHEMATICAL & COMPUTATIONAL BIOLOGY	1