Progress of bioinformatics studies for multi-omics and multi-modal data in complex diseases

With the advancement of high-throughput sequencing technologies, the integration of multi-omics and multi-modal data has become an important trend in the study of complex diseases. Multi-omics/multi-modal data provide new perspectives for a deeper understanding of the pathogenesis and development of diseases, offering crucial support for the precision diagnosis and treatment of complex diseases. This review first introduces various types of omics data and their contributions to complex disease research. Genomics reveals the genetic background and mutations associated with diseases by analyzing gene sequences; transcriptomics uncovers gene regulatory relationships related to diseases by studying expression patterns; proteomics focuses on the expression, modification, and interactions of proteins; metabolomics reflects adjustments in metabolic pathways before and after illness through changes in metabolites; radiomics shows disease-induced alterations via medical imaging. Integrating and analyzing these omics data can compensate for the information gaps of single omics data, enabling a more comprehensive understanding of the molecular mechanisms of complex diseases. Furthermore, this review introduces multi-omics databases related to complex diseases, covering diseases such as cancer, cardiovascular and cerebrovascular diseases, organ fibrosis, chronic kidney disease, Alzheimer’s disease, and inflammatory bowel disease. These databases facilitate researchers in obtaining and analyzing multi-omics data. Next, this review systematically categorizes the existing multi-omics integration methods into two types: correlation and network-based methods and data matrix and machine learning-based methods. These two approaches use different means to reveal the potential connections between data, thereby providing deeper insights into complex biological systems. Correlation and network-based methods involve using association analysis or complex network analysis to identify the intrinsic connections between different omics, thereby discovering biomarkers related to phenotypes. Data matrix and machine learning-based methods refer to utilizing statistical analysis, machine learning, and deep learning models to achieve data fusion for clustering or classification tasks, while revealing the inherent relationships between multi-omics data and identifying disease-related biomarkers. Data matrix and machine learning-based methods are further divided into early integration, intermediate integration, and late integration. Early integration method involves merging multi-omics data into a joint matrix and then applying machine learning or deep learning models for classification. Intermediate integration method involves modeling each omics data separately, followed by the integration of the transformed matrices or models. Late integration method independently models each omics data and then combines the model output results. Building on this, the review also discusses the applications of multi-omics integration models in complex diseases, such as disease screening, subtyping, prognosis, and drug response prediction. Finally, this review summarizes the current challenges in multi-omics/multi-modal data integration, which are divided into three levels: sample, data, and model. At the sample level, the absence of matching data limits the efficacy of integration methods, and researchers are addressing this issue through the development of data sharing and new algorithms. At the data level, the characteristics of high dimensionality, noise, and heterogeneity necessitate the use of more efficient deep learning methods for data integration. At the model level, the key challenges include lack of interpretability, low computational efficiency, and privacy concerns. Researchers are enhancing model interpretability through visualization tools and incorporating biological prior knowledge into deep learning models, while also exploring new technologies such as model acceleration and privacy-preserving computation to improve model efficiency and security. Despite the challenges, multi-omics integration has demonstrated significant potential in the diagnosis and treatment of complex diseases.