Key Takeaways & Executive Findings
- •• Integrative multi-omics approaches significantly enhance the accuracy of disease subtyping and biomarker discovery compared to single-omics analyses. • Deep learning and network-based models are pivotal in managing high-dimensional, heterogeneous omics data, enabling robust predictive modeling. • Addressing data heterogeneity and missingness through advanced imputation and normalization techniques is critical for reliable cross-omics integration. • Successful clinical translation of multi-omics requires standardized protocols and interdisciplinary collaboration to ensure reproducibility and scalability.
Abstract
The rapid evolution of high-throughput technologies has generated an unprecedented wealth of biological data, necessitating sophisticated integrative approaches to translate this information into actionable clinical insights. This comprehensive review, conducted under the auspices of the SinoBioData Intelligence Archive, synthesizes recent advancements in multi-omics data integration, with a particular focus on genomics, transcriptomics, proteomics, and metabolomics. We systematically evaluate state-of-the-art computational frameworks, including deep learning architectures and network-based models, that facilitate the holistic interpretation of complex biological systems. Our analysis highlights the pivotal role of integrative multi-omics in elucidating disease mechanisms, identifying novel biomarkers, and guiding personalized therapeutic strategies. Furthermore, we address critical challenges such as data heterogeneity, missingness, and scalability, proposing robust solutions grounded in recent methodological innovations. By examining landmark studies and emerging trends, we underscore the transformative potential of multi-omics integration in precision medicine, while acknowledging the necessity for standardized protocols and interdisciplinary collaboration. This review serves as a seminal resource for researchers and clinicians aiming to harness the full spectrum of omics data to improve patient outcomes and advance biomedical knowledge.
1. Introduction
The advent of high-throughput technologies has revolutionized biomedical research, enabling the comprehensive profiling of molecular entities across multiple layers of biological organization. Genomics, transcriptomics, proteomics, and metabolomics each provide a unique perspective on cellular function and disease pathology. However, the sheer volume and complexity of these data present significant analytical challenges. Traditional single-omics analyses, while valuable, often fail to capture the intricate interplay between molecular layers that underlies complex phenotypes. Consequently, there is a growing imperative to develop integrative multi-omics frameworks that can synthesize diverse data types into a coherent biological narrative.
In this review, we present a comprehensive overview of recent advancements in multi-omics data integration, with a focus on applications in precision medicine. We explore computational strategies that facilitate the joint analysis of omics datasets, ranging from classical statistical approaches to cutting-edge machine learning techniques. Moreover, we discuss the practical implications of these methods in clinical settings, including disease subtyping, biomarker discovery, and therapeutic response prediction. By synthesizing current knowledge and identifying future directions, we aim to provide a valuable resource for researchers and clinicians seeking to leverage multi-omics data to improve patient care.
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CHEN Yu, WANG Fang, LIU Jing, ZHAO Min (2026). Advancements in SinoBioData: A Comprehensive Review of Integrative Multi-Omics Approaches in Precision Medicine. Chinese Traditional and Herbal Drugs. https://doi.org/10.7501/j.issn.0253-2670.2026.12.2026120
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Frequently Asked Questions
What is multi-omics data integration and why is it important?
Multi-omics data integration refers to the combined analysis of data from different omics layers, such as genomics, transcriptomics, proteomics, and metabolomics. It is important because it provides a more holistic view of biological systems, enabling the identification of complex molecular interactions and improving the accuracy of disease diagnosis, prognosis, and treatment response prediction.
What are the main computational challenges in integrating multi-omics data?
Key challenges include data heterogeneity (different types and scales), missingness, high dimensionality, and scalability. Advanced methods such as deep learning and network-based models are being developed to address these issues, along with robust imputation and normalization techniques.
How does multi-omics integration contribute to precision medicine?
By integrating multiple omics layers, researchers can identify more precise molecular subtypes of diseases, discover novel biomarkers, and predict individual patient responses to therapies. This enables the tailoring of medical treatments to the specific molecular profile of each patient, thereby improving efficacy and reducing adverse effects.
What are the future directions in multi-omics research?
Future directions include the development of standardized protocols for data acquisition and analysis, the incorporation of single-cell omics data, the integration of clinical and environmental data, and the application of advanced artificial intelligence methods to enhance predictive power and clinical utility.
What is the role of the SinoBioData Intelligence Archive in this field?
The SinoBioData Intelligence Archive serves as a central repository and analytical platform for biomedical data, facilitating the integration and analysis of multi-omics datasets. It supports research by providing curated data, computational tools, and expertise, thereby accelerating discoveries in precision medicine.
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