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How does multi-omics integration reveal disease mechanisms? ​

Abstract ​

The integration of multi-omics data has emerged as a revolutionary approach in biomedical research, offering profound insights into the complex biological mechanisms underlying various diseases. Multi-omics encompasses the systematic analysis of diverse biological layers, including genomics, transcriptomics, proteomics, metabolomics, and epigenomics, enabling researchers to achieve a holistic understanding of cellular functions and interactions. This approach addresses the limitations of single-omics studies, which often provide fragmented insights into disease processes. By synthesizing data across multiple omics layers, multi-omics integration facilitates the identification of novel biomarkers and therapeutic targets, enhancing the predictive power of models used in precision medicine. Notably, this methodology has shown particular promise in oncology, cardiovascular diseases, and neurodegenerative disorders, where intricate interactions among genetic, epigenetic, and environmental factors play critical roles in disease progression. Recent advancements in high-throughput technologies and computational methodologies have propelled the field forward, enabling sophisticated data integration techniques. However, challenges such as data standardization, harmonization of integration techniques, and the application of artificial intelligence to optimize analyses remain. This review synthesizes recent findings and emphasizes the transformative potential of multi-omics integration in deepening our understanding of disease mechanisms and informing the development of novel therapeutic strategies. Ultimately, the integration of multi-omics data not only enhances our biological understanding but also holds the promise of revolutionizing clinical practice through the identification of precise biomarkers and personalized treatment options.

Outline ​

This report will discuss the following questions.

  • 1 引言
  • 2 Overview of Multi-Omics Integration
    • 2.1 Definition and Importance of Multi-Omics
    • 2.2 Types of Omics Data and Their Relevance
  • 3 Methodologies for Multi-Omics Integration
    • 3.1 Data Acquisition and Preprocessing
    • 3.2 Integration Techniques: Statistical and Computational Approaches
  • 4 Applications in Disease Mechanisms
    • 4.1 Case Study: Cancer
    • 4.2 Case Study: Cardiovascular Diseases
    • 4.3 Case Study: Neurodegenerative Disorders
  • 5 Challenges and Future Directions
    • 5.1 Data Standardization and Harmonization
    • 5.2 Role of Artificial Intelligence in Multi-Omics
    • 5.3 Future Perspectives in Precision Medicine
  • 6 Summary

1 Introduction ​

The integration of multi-omics data has emerged as a transformative approach in biomedical research, providing unprecedented insights into the complex biological mechanisms underlying various diseases. Multi-omics encompasses the systematic study of diverse biological layers, including genomics, transcriptomics, proteomics, metabolomics, and epigenomics, allowing researchers to capture a holistic view of cellular functions and interactions. As the limitations of single-omics studies become increasingly apparent, the necessity for integrated analyses that elucidate the intricate interplay among these layers has gained prominence. For instance, while genomic data may reveal mutations associated with disease, transcriptomic and proteomic analyses can uncover how these mutations manifest in cellular behavior and contribute to disease pathology [1][2].

The significance of multi-omics integration lies in its potential to enhance our understanding of disease mechanisms, ultimately paving the way for improved diagnostic and therapeutic strategies. Traditional methods of studying diseases often focus on isolated pathways or molecular components, leading to fragmented knowledge that fails to capture the comprehensive nature of biological systems. Multi-omics approaches, however, facilitate the identification of novel biomarkers and therapeutic targets by integrating data from various sources, thereby enhancing the predictive power of models used in precision medicine [3][4]. The application of multi-omics has shown particular promise in fields such as oncology, cardiovascular diseases, and neurodegenerative disorders, where complex interactions among genetic, epigenetic, and environmental factors play crucial roles in disease progression [5][6].

Recent advancements in high-throughput technologies and computational methodologies have propelled the field of multi-omics forward, allowing for more sophisticated data integration techniques. These methodologies range from statistical approaches to machine learning algorithms, enabling researchers to tackle the challenges posed by the high dimensionality and heterogeneity of multi-omics data [7][8]. Despite these advancements, several obstacles remain, including the need for data standardization, harmonization of integration techniques, and the application of artificial intelligence to optimize analyses [9][10].

This review is organized as follows: Section 2 provides an overview of multi-omics integration, including its definition, importance, and the types of omics data involved. Section 3 discusses the methodologies employed for multi-omics integration, focusing on data acquisition, preprocessing, and various integration techniques. In Section 4, we explore the applications of multi-omics in elucidating disease mechanisms, with case studies on cancer, cardiovascular diseases, and neurodegenerative disorders. Section 5 addresses the challenges faced in this evolving field and outlines future directions, particularly in the context of precision medicine. Finally, Section 6 summarizes the key findings and implications of multi-omics integration for biomedical research.

By synthesizing recent findings and highlighting the transformative potential of multi-omics integration, this review aims to provide a comprehensive understanding of how these approaches can deepen our insights into disease mechanisms and inform the development of novel therapeutic strategies. The integration of multi-omics data not only enhances our biological understanding but also holds the promise of revolutionizing clinical practice through the identification of precise biomarkers and personalized treatment options.

2 Overview of Multi-Omics Integration ​

2.1 Definition and Importance of Multi-Omics ​

Multi-omics integration refers to the comprehensive analysis of various biological layers, including genomics, transcriptomics, proteomics, metabolomics, and microbiomics, to gain a holistic understanding of complex diseases. This approach is crucial for revealing disease mechanisms as it allows researchers to examine how different molecular levels interact and contribute to disease processes.

The significance of multi-omics lies in its ability to overcome the limitations of single-omics studies, which often provide only fragmented insights into biological mechanisms. For instance, while genomics can reveal genetic variations associated with diseases, it may not capture the dynamic interactions between genes, proteins, and metabolites that ultimately influence disease outcomes. Multi-omics integration addresses this gap by synthesizing data across multiple omics layers, thus providing a more comprehensive view of the biological systems involved in health and disease (Menyhárt & Győrffy, 2021; Wu et al., 2021).

One of the primary advantages of multi-omics integration is its capacity to identify novel disease mechanisms and potential therapeutic targets. By combining data from various omics sources, researchers can uncover interactions and pathways that might not be apparent when analyzing each layer in isolation. For example, in the study of methylmalonic aciduria (MMA), a metabolic disorder, multi-omics integration revealed critical insights into the role of glutathione metabolism and lysosomal function in the disease's pathogenesis (Fu et al., 2025). Such findings are facilitated by advanced data integration strategies that allow for the correlation of genomic, transcriptomic, proteomic, and metabolomic data with clinical outcomes.

Furthermore, multi-omics integration plays a pivotal role in the classification and prognostication of diseases. It enables the identification of specific molecular signatures associated with different disease subtypes, thereby improving diagnostic accuracy and treatment strategies. This approach has been particularly beneficial in cancer research, where integrated analyses of histopathology, transcriptomics, and proteomics have unveiled compartment-specific biology and emergent molecular patterns related to tumor progression (Junquera & Färkkilä, 2025).

However, the integration of multi-omics data is not without challenges. Technical hurdles such as data heterogeneity, platform variability, and the "curse of dimensionality" complicate the analysis. To address these issues, researchers have developed various integration strategies, including data-driven and knowledge-guided approaches, which utilize dimensionality reduction techniques and computational tools to facilitate the synthesis of multi-omics datasets (Lu et al., 2025).

In summary, multi-omics integration is a powerful approach that enhances our understanding of disease mechanisms by elucidating the interactions among different biological layers. It provides a framework for discovering novel biomarkers, therapeutic targets, and insights into the complex etiology of diseases, thereby advancing precision medicine and improving patient outcomes. The ongoing development of standardized analytical pipelines and interdisciplinary training for data interpretation is essential for the successful clinical application of multi-omics strategies (Menyhárt & Győrffy, 2021; Wu et al., 2021; Fu et al., 2025).

2.2 Types of Omics Data and Their Relevance ​

Multi-omics integration is a cutting-edge approach that combines various omics data types—such as genomics, transcriptomics, proteomics, metabolomics, and microbiomics—to provide a comprehensive understanding of complex biological systems and disease mechanisms. By integrating these different layers of biological information, researchers can uncover intricate interactions and pathways that are not apparent when examining single omics data in isolation.

The relevance of different types of omics data in understanding disease mechanisms is substantial. For instance, genomics provides insights into the genetic variations that may predispose individuals to certain diseases, while transcriptomics reveals the gene expression profiles that indicate how these genetic variations manifest at the cellular level. Proteomics, on the other hand, examines the protein expressions and modifications that are crucial for cellular functions and responses, while metabolomics focuses on the small molecule metabolites that reflect the biochemical activity within cells. Each of these omics layers contributes a unique perspective, which, when combined, can lead to a more holistic view of disease pathogenesis.

One significant advantage of multi-omics integration is its ability to elucidate the interactions between different biological systems. For example, in cancer research, multi-omics strategies have been employed to identify driver genomic alterations and classify tumor subtypes, thereby enhancing the understanding of tumor biology and informing prognostic and therapeutic decisions[1].

Moreover, advancements in high-throughput technologies have facilitated the generation of large datasets across multiple omics levels. However, the integration of these diverse data types poses significant challenges due to issues such as data heterogeneity and the complexity of biological interactions. Researchers have developed various integration strategies, including data-driven and knowledge-guided approaches, to address these challenges and extract meaningful biological insights[10].

In the context of specific diseases, multi-omics integration has proven invaluable. For instance, in small-cell lung cancer (SCLC), the integration of genomics, transcriptomics, and proteomics has led to a deeper understanding of the disease's pathogenesis and has facilitated the identification of novel therapeutic targets[5]. Similarly, multi-omics analyses have been instrumental in understanding the mechanisms underlying aging and its related diseases, revealing new biomarkers and anti-aging targets through the systematic exploration of molecular interactions[2].

The integration of omics data also enhances the predictive capabilities regarding disease outcomes. Frameworks such as GREMI, which utilize graph attention architectures to incorporate biomolecular interaction information, have demonstrated improved performance in disease prediction and module identification[8]. This capability is particularly important for precision medicine, where understanding the specific molecular underpinnings of a patient's disease can lead to tailored treatment strategies.

In summary, multi-omics integration reveals disease mechanisms by providing a multidimensional perspective that encompasses various biological layers. This approach not only enhances the understanding of complex diseases but also aids in the identification of novel biomarkers and therapeutic targets, ultimately contributing to advancements in precision medicine and improved patient outcomes. The continued development of integration methodologies and the refinement of analytical techniques will further enhance the utility of multi-omics in biomedical research[3][4].

3 Methodologies for Multi-Omics Integration ​

3.1 Data Acquisition and Preprocessing ​

Multi-omics integration plays a crucial role in elucidating disease mechanisms by combining data from various biological layers, including genomics, transcriptomics, proteomics, metabolomics, and microbiomics. This holistic approach allows researchers to capture the complexity of biological systems and uncover interactions that single-omics studies may overlook.

The methodologies for multi-omics integration can be categorized into several approaches, including data-driven and knowledge-guided strategies. Data-driven methods utilize statistical and computational techniques to combine datasets, while knowledge-guided approaches rely on existing biological knowledge to inform integration processes. These methods are essential for addressing challenges such as data heterogeneity and dimensionality, which can complicate the integration of diverse omics data [10].

Data acquisition in multi-omics studies typically involves high-throughput technologies that generate large volumes of data. This includes sequencing technologies for genomics and transcriptomics, mass spectrometry for proteomics and metabolomics, and various platforms for microbiome analysis. Each omics layer provides unique insights into the biological processes involved in disease, but the sheer volume and complexity of the data necessitate robust preprocessing techniques to ensure data quality and compatibility [[pmid:33613862],[pmid:39110558]].

Preprocessing steps often include normalization, transformation, and filtering to remove noise and irrelevant information from the datasets. Additionally, alignment of different omics data types is crucial for effective integration, as it allows for the correlation of findings across layers. For instance, integrating transcriptomic data with proteomic data can help validate the functional relevance of differentially expressed genes by correlating them with protein expression levels [[pmid:35053186],[pmid:40436252]].

Moreover, the integration of multi-omics data enables the identification of molecular pathways and networks that contribute to disease mechanisms. By leveraging computational tools and algorithms, researchers can analyze interactions among various biomolecules and pinpoint key players involved in disease progression. For example, multi-omics integration has been successfully applied to understand complex diseases such as cancer, where it helps classify tumor subtypes and identify potential therapeutic targets [[pmid:41260975],[pmid:37873876]].

In summary, multi-omics integration reveals disease mechanisms by combining diverse datasets to provide a comprehensive view of biological processes. Methodologies for integration include data-driven and knowledge-guided approaches, while effective data acquisition and preprocessing are vital for ensuring the quality and compatibility of the data. This integrated approach not only enhances our understanding of disease mechanisms but also facilitates the identification of novel biomarkers and therapeutic targets [[pmid:37533767],[pmid:39103035]].

3.2 Integration Techniques: Statistical and Computational Approaches ​

Multi-omics integration employs various statistical and computational methodologies to unveil the complex mechanisms underlying diseases. By combining data from multiple biological layers, such as genomics, transcriptomics, proteomics, and metabolomics, researchers can achieve a comprehensive understanding of the interactions and functions of different biomolecules within a biological system. This holistic view is crucial for elucidating the pathophysiology of complex diseases.

Recent findings emphasize the utility of multi-omics approaches in improving disease diagnosis, prognosis, and treatment. For instance, integration strategies can prioritize causal genes and clarify the roles of non-coding variants associated with disease. Techniques like Mendelian randomization leverage genetic data to provide evidence supporting the causal roles of disease-associated molecules, thereby elucidating their contributions to disease pathogenesis (Mohammadi-Shemirani et al., 2023) [11].

To effectively integrate multi-omics data, several computational methods have been developed. These methods can be broadly categorized into data-driven and knowledge-guided approaches. Data-driven strategies often involve dimensionality reduction techniques that simplify complex datasets, making them more manageable for analysis. Knowledge-guided approaches, on the other hand, utilize existing biological knowledge to inform data integration, allowing researchers to derive more meaningful insights from the combined datasets (Lu et al., 2025) [10].

Moreover, machine learning techniques have emerged as powerful tools in multi-omics integration. They facilitate the identification of patterns and relationships within high-dimensional data, which can be crucial for discovering novel biomarkers and therapeutic targets. For example, integrating machine learning with multi-omics data can enhance the precision of disease predictions and improve the selection of personalized treatment options (Ivanisevic & Sewduth, 2023) [4].

Despite the promising potential of multi-omics integration, challenges remain. The high dimensionality and heterogeneity of multi-omics datasets complicate their analysis, and there is often a lack of standardized protocols for data integration. To address these issues, researchers are developing robust methodologies that can effectively handle the complexities associated with multi-omics data, including the establishment of frameworks for simultaneous or step-wise integration of various omics layers (Wörheide et al., 2021) [12].

In summary, multi-omics integration reveals disease mechanisms by employing a variety of statistical and computational approaches that allow for the synthesis of diverse biological data. This integration enhances the understanding of disease pathology, facilitates the identification of biomarkers, and ultimately supports the advancement of precision medicine strategies. As methodologies continue to evolve, the potential for multi-omics to transform disease research and clinical practice will only grow.

4 Applications in Disease Mechanisms ​

4.1 Case Study: Cancer ​

Multi-omics integration represents a transformative approach in cancer research, enabling a comprehensive understanding of the disease mechanisms through the integration of various biological layers, including genomics, transcriptomics, proteomics, metabolomics, and microbiomics. This multifaceted strategy provides unparalleled insights into the complexities of cancer biology, allowing researchers to elucidate the intricate pathways involved in tumorigenesis and progression.

The integration of multi-omics data allows for the identification of causal relationships and the unveiling of the molecular underpinnings of cancer. For instance, innovative multi-omics frameworks facilitate the classification of cancers into distinct subtypes, which is crucial for refining survival predictions and treatment outcomes. Advanced algorithms are employed to analyze these diverse datasets, revealing cancer subtypes, disease mechanisms, and pivotal genomic alterations that drive malignancy [13].

One significant application of multi-omics integration in understanding cancer mechanisms is its ability to uncover the heterogeneity within tumors. By combining data from various omics layers, researchers can construct detailed tumor ecosystem landscapes, which elucidate the interactions between cancer cells and their microenvironment. This holistic view is essential for identifying novel therapeutic targets and developing personalized treatment strategies [14].

A notable case study can be observed in the context of small-cell lung cancer (SCLC), where multi-omics analysis has become an essential research direction. This approach integrates genomic, transcriptomic, and proteomic data to provide a comprehensive understanding of the pathogenesis and development of SCLC. Such integration aids in the identification of novel therapeutics and biomarkers, ultimately enhancing precision oncology efforts [5].

Furthermore, multi-omics strategies enable the exploration of spatial and temporal dynamics within tumors. For example, the integration of histopathology with transcriptomics and proteomics allows researchers to reveal compartment-specific biology and emergent molecular patterns that underlie malignant transformation. This capability is crucial for guiding precision oncology by uncovering mechanisms that are otherwise obscured when examining single layers of data [15].

Despite the advantages of multi-omics integration, challenges remain in its clinical implementation. The uneven maturity of different omics approaches and the disparity between data generation and processing capacity pose significant obstacles. Nevertheless, ongoing initiatives aimed at standardizing sample processing and analytical pipelines, along with multidisciplinary training for data analysis, are vital for translating theoretical findings into clinical practice [1].

In summary, multi-omics integration provides a powerful framework for revealing disease mechanisms in cancer by enabling a comprehensive understanding of the molecular and clinical features of the disease. This integrative approach not only enhances our knowledge of tumor biology but also paves the way for improved diagnostics, prognostics, and therapeutic strategies, ultimately contributing to the advancement of precision medicine in oncology.

4.2 Case Study: Cardiovascular Diseases ​

Multi-omics integration is a powerful approach that combines data from various biological layers, including genomics, transcriptomics, proteomics, metabolomics, and epigenomics, to provide a comprehensive understanding of disease mechanisms. This methodology is particularly beneficial in the context of cardiovascular diseases (CVD), where complex interactions among genetic, environmental, and biological factors contribute to disease phenotypic heterogeneity.

Recent studies emphasize the role of multi-omics technologies in elucidating the intricate molecular mechanisms underlying cardiovascular diseases. For instance, Wang et al. (2023) highlight that CVDs are influenced by numerous convergent processes, including genetic variation and environmental factors, which can be better understood through the integration of multi-omics data. The authors note that while many associated genes and genetic loci have been identified, the precise mechanisms through which these genetic factors influence CVD phenotypes remain unclear. By leveraging data from various omics levels, researchers can gain insights into the epigenome, transcriptome, proteome, and metabolome, thus facilitating more precise diagnosis and personalized treatment strategies[16].

In a case study focusing on heart failure, Esquivel Gaytan et al. (2024) argue that integrating multi-omics technologies with systems medicine approaches can significantly enhance heart failure management. The traditional paradigm of treatment, which often relies on a "one size fits all" approach, fails to account for the molecular diversity of the disease. By employing a comprehensive multi-omics strategy, researchers can uncover the molecular foundations of heart failure, leading to improved disease classification and personalized risk assessments. This shift towards a universal approach using quantitative multi-omics data enables a more profound understanding of the complex molecular interactions that characterize heart failure[17].

Additionally, Sopic et al. (2023) discuss the integration of epigenetic regulatory mechanisms in heart failure, emphasizing that multi-omics approaches are essential for tackling complex diseases like CVD. They provide an overview of how different epigenetic modifications, such as DNA and histone modifications, regulate gene expression and contribute to the development of cardiac diseases. By integrating these epigenetic insights with other omics data, researchers can construct a more holistic view of the disease's underlying mechanisms, potentially leading to novel therapeutic strategies and biomarkers[18].

Moreover, multi-omics integration facilitates the identification of causal mediators of disease. For instance, Mohammadi-Shemirani et al. (2023) point out that multi-omics approaches enhance predictive power and causal inference by combining data from different omics layers. This integrative strategy allows for a deeper understanding of the biological mechanisms at play, as it provides orthogonal evidence that can clarify the roles of specific genes and metabolites in disease pathogenesis[11].

In summary, multi-omics integration reveals disease mechanisms by providing a comprehensive and interconnected view of the biological processes involved in cardiovascular diseases. This approach not only enhances our understanding of the molecular underpinnings of these diseases but also paves the way for more personalized and effective treatment strategies, ultimately improving patient outcomes. The ongoing advancements in multi-omics technologies and data integration methodologies continue to open new avenues for research and clinical application in cardiovascular medicine.

4.3 Case Study: Neurodegenerative Disorders ​

Multi-omics integration plays a pivotal role in elucidating disease mechanisms, particularly in the context of neurodegenerative disorders. This approach involves the simultaneous analysis of various omics layers, including genomics, transcriptomics, proteomics, metabolomics, and epigenomics, which collectively provide a comprehensive view of the biological processes underlying these complex diseases.

In neurodegenerative disorders, multi-omics integration enables researchers to generate new hypotheses regarding the differential regulation of biological molecules associated with disease mechanisms. For instance, bioinformatic analysis of large and complex omics datasets has been increasingly applied in neuroscience, referred to as neuroinformatics. By integrating multi-omics data, researchers can advance the understanding of the layered and interactive networks of biological regulation, facilitating the development of a comprehensive human brain profile that is crucial for addressing conditions such as Alzheimer's disease, Parkinson's disease, and multiple sclerosis [19].

A notable application of multi-omics integration in neurodegenerative diseases is the identification of molecular biomarkers that can be used for early diagnosis and therapeutic targeting. The integration of different omics data, as emphasized by the Italian proteomics community, is essential for discovering biomarkers that allow for patient stratification and prediction of treatment responses [20]. This is particularly significant given the growing number of patients affected by neurodegenerative disorders, which poses substantial challenges to healthcare systems.

Moreover, multi-omics approaches provide insights into the complex interactions between various biological systems. For example, recent studies have highlighted the utility of integrating transcriptomic, epigenomic, and proteomic data to uncover novel molecular interactions and spatial tissue organization in diseases like diabetic kidney disease and autosomal polycystic kidney disease [21]. This holistic perspective is crucial for understanding the multifactorial nature of neurodegenerative disorders, where both genetic and environmental factors interplay.

Additionally, the application of machine learning and artificial intelligence in analyzing multi-omics data enhances the ability to predict disease outcomes and identify meaningful biomarkers. For instance, frameworks such as GREMI focus on multi-omics classification and explanation, aiming to improve prediction performance while providing a deeper understanding of the biological interactions influencing disease outcomes [8]. This is particularly relevant in neurodegenerative diseases, where understanding the underlying mechanisms can lead to the development of targeted therapies.

In summary, multi-omics integration reveals disease mechanisms in neurodegenerative disorders by providing a comprehensive view of the biological processes involved. It facilitates the discovery of biomarkers, enhances understanding of molecular interactions, and supports the development of precision medicine strategies aimed at improving patient outcomes. As the field continues to evolve, the integration of multi-omics approaches is expected to play an increasingly vital role in the elucidation of complex disease mechanisms and the advancement of therapeutic interventions.

5 Challenges and Future Directions ​

5.1 Data Standardization and Harmonization ​

Multi-omics integration serves as a powerful approach to uncovering the intricate mechanisms underlying various diseases by synthesizing data from multiple biological layers, including genomics, transcriptomics, proteomics, and metabolomics. This holistic view enhances the understanding of disease pathogenesis and allows for the identification of novel biomarkers and therapeutic targets. However, the process of integrating multi-omics data is fraught with challenges, particularly concerning data standardization and harmonization.

One of the primary challenges in multi-omics integration is the heterogeneity of data generated from different omics technologies. Each omics layer has its own unique characteristics, including variations in data types, scales, and measurement techniques, which can complicate the integration process. The lack of standardized protocols for sample collection, processing, and analysis further exacerbates these issues. For instance, differences in laboratory practices and bioinformatics pipelines can lead to discrepancies in data quality and comparability, ultimately hindering the integration efforts[1].

Moreover, the high dimensionality of multi-omics data poses significant computational challenges. As datasets grow in size and complexity, the need for advanced analytical methods becomes crucial. Traditional statistical approaches may not be sufficient to handle the intricate relationships between different omics layers, necessitating the development of new algorithms and computational tools that can effectively integrate and analyze these data[22].

To address these challenges, there is a pressing need for the establishment of standardized frameworks and guidelines for multi-omics data generation and integration. Initiatives aimed at promoting best practices in data handling, including the standardization of data formats and analytical methods, are vital. This would facilitate the comparability of results across studies and enhance the reproducibility of findings[3].

Additionally, the application of semantic technologies has emerged as a promising strategy to improve data integration and standardization. By utilizing ontologies and knowledge graphs, researchers can enhance the interoperability of multi-omics datasets, allowing for more effective data sharing and collaboration across different research groups[23]. Such technologies can help in organizing and integrating diverse data types, thus paving the way for more robust analyses and insights into disease mechanisms.

Looking ahead, future research should focus on developing advanced computational tools that incorporate artificial intelligence and machine learning techniques to facilitate the integration of multi-omics data. These approaches can provide new ways to analyze complex datasets, identify patterns, and generate predictive models that can inform personalized medicine[9]. Furthermore, fostering collaborations between clinicians, researchers, and bioinformaticians will be essential to overcoming the existing barriers and fully leveraging the potential of multi-omics integration in understanding disease mechanisms and improving clinical outcomes[24].

In conclusion, while multi-omics integration presents significant opportunities for elucidating disease mechanisms, the challenges related to data standardization and harmonization must be addressed through concerted efforts in establishing best practices, leveraging semantic technologies, and developing innovative computational methodologies. Such advancements will be crucial in enhancing the translatability of multi-omics findings into clinical applications.

5.2 Role of Artificial Intelligence in Multi-Omics ​

Multi-omics integration is a powerful approach that combines data from various biological layers, such as genomics, transcriptomics, proteomics, and metabolomics, to gain a comprehensive understanding of disease mechanisms. This integration allows researchers to elucidate complex biological processes and identify the interplay between different molecular components involved in diseases. For instance, multi-omics data integration strategies can reveal causal relationships and provide insights into tumor subtyping, disease mechanisms, and the identification of driver genomic alterations, significantly enhancing our understanding of malignant transformations and their outcomes (Menyhárt & Győrffy, 2021) [1].

However, the integration of multi-omics data presents several challenges. One major obstacle is the uneven maturity of different omics approaches, which can create discrepancies in data quality and processing capacity. As highlighted by Zhou et al. (2023), the integration of multi-omics is often slow to enter clinical practice due to these challenges, necessitating the standardization of sample processing and analytical pipelines, as well as multidisciplinary training for data analysis and interpretation [25]. Additionally, the complexity and vast volume of data generated by multi-omics technologies require sophisticated computational methods for effective analysis and interpretation (Lin et al., 2025) [26].

Artificial intelligence (AI) plays a crucial role in addressing these challenges by facilitating the integration and analysis of multi-omics data. AI-driven bioinformatics can compute scores to prioritize available drugs, aiding clinicians in selecting optimal treatments based on individual omics profiles. This capability is essential for advancing precision medicine, as it enables early disease detection and prevention, aids in biomarker discovery for diagnosis and prognosis, and identifies molecular targets for innovative drug development (Srivastava, 2025) [27]. Furthermore, AI methods can enhance data-driven knowledge discovery and causal inference, which are critical for translating multi-omics research into clinical practice (Lin et al., 2025) [26].

In summary, multi-omics integration provides a holistic view of disease mechanisms, although it faces significant challenges related to data maturity and complexity. The application of AI in this context not only aids in overcoming these challenges but also enhances the potential for precision medicine, thereby paving the way for innovative therapeutic strategies and improved patient outcomes. As the field progresses, the integration of AI and multi-omics is anticipated to yield new insights and applications in various domains of biomedical research and clinical practice.

5.3 Future Perspectives in Precision Medicine ​

Multi-omics integration plays a pivotal role in elucidating disease mechanisms by synthesizing data from various biological layers, including genomics, transcriptomics, proteomics, metabolomics, and epigenomics. This integrative approach enables a comprehensive understanding of the complex interactions and regulatory networks that govern biological systems, thereby enhancing the precision of disease characterization and treatment strategies.

The integration of multi-omics data provides deep insights into disease-associated molecular mechanisms, which are crucial for advancing precision medicine. For instance, it allows for the identification of molecular targets for innovative drug development or the repurposing of existing therapies. By accounting for individual omics profiles, multi-omics facilitates early disease detection and prevention, as well as aids in biomarker discovery for diagnosis, prognosis, and treatment monitoring [27].

Despite its promise, the quantitative integration of multi-omics data presents significant challenges. The high-dimensionality and heterogeneity of data, along with the frequent occurrence of missing values, complicate the integration process [28]. Moreover, current methodologies often focus on maximizing prediction accuracy while neglecting the identification of meaningful biomarkers, which is essential for understanding disease outcomes [8].

Future directions in the field of multi-omics integration involve addressing these challenges through advanced computational methods, such as machine learning and deep learning techniques. For example, variational autoencoders (VAEs) have been highlighted as effective tools for data imputation, augmentation, and batch effect correction, thus improving the integration of diverse omics datasets [28]. Furthermore, the emergence of spatial multi-omics technologies has enhanced the understanding of cell biology and the molecular basis of human diseases by providing insights into spatial heterogeneity and intercellular interactions [29].

In terms of future perspectives, the integration of AI-driven bioinformatics into multi-omics approaches is expected to further transform precision medicine. AI can assist in prioritizing available drugs and optimizing treatment selection, thereby facilitating personalized interventions [27]. Additionally, the convergence of systems biology and multi-omics with AI holds the potential to redefine biomarker discovery and drive early disease detection, moving health care from reactive treatment to proactive wellness [30].

The clinical implementation of multi-omics remains a critical area for development. Efforts must focus on standardizing sample processing and analytical pipelines, as well as training multidisciplinary experts to analyze and interpret the complex data generated [1]. These advancements will not only enhance the translatability of theoretical findings into clinical practice but also improve patient outcomes through more personalized and effective therapeutic strategies [31].

In conclusion, multi-omics integration serves as a powerful framework for uncovering disease mechanisms and holds immense potential for advancing precision medicine. The ongoing development of computational tools, alongside the integration of AI and systems biology, will be crucial in overcoming current challenges and unlocking new opportunities for personalized healthcare solutions.

6 Conclusion ​

The integration of multi-omics data is a transformative approach that significantly enhances our understanding of disease mechanisms across various biomedical fields. Key findings indicate that multi-omics integration not only uncovers novel biomarkers and therapeutic targets but also elucidates complex interactions among genetic, epigenetic, and environmental factors influencing disease progression. Current research demonstrates its application in oncology, cardiovascular diseases, and neurodegenerative disorders, highlighting the potential for improved diagnostic and therapeutic strategies. However, challenges remain, particularly regarding data standardization, integration methodologies, and the need for interdisciplinary collaboration. Future research should focus on refining computational techniques, leveraging artificial intelligence for data analysis, and fostering collaborations between clinicians and researchers to maximize the clinical utility of multi-omics approaches. This will ultimately pave the way for personalized medicine, transforming patient care through targeted interventions and improved health outcomes.

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