Table of Contents
Overview
Definition of Omics
Omics refers to fields in biological sciences that study entire sets of biological molecules to understand their roles in the structure, function, and dynamics of organisms. The suffix "-omics" is used in disciplines such as genomics, proteomics, metabolomics, metagenomics, phenomics, and transcriptomics, which aim to identify, describe, and quantify biomolecules and molecular processes within biological systems.[3.1] This comprehensive analysis focuses on specific types of biomolecules, such as genomes, proteomes, and metabolomes.[3.1] For example, genomics examines the structure, function, and evolution of genomes, while proteomics studies the complete set of proteins produced by an organism.[4.1] This holistic approach provides insights into biological mechanisms, including disease processes and potential therapeutic targets.[5.1]Importance of Omics in Modern Science
Omics technologies are pivotal in modern science, offering comprehensive analyses of biological systems by integrating various biological data types. Multiomics, which includes genomics, transcriptomics, proteomics, metabolomics, and epigenomics, enables researchers to explore cellular and organismal interactions and pathways. This approach enhances our understanding of disease mechanisms and biomarker discovery, providing insights into the molecular basis of health and disease by combining data from multiple biological domains, such as transcripts, proteins, and metabolites.[13.1] [19.1] Advancements in DNA sequencing technologies have greatly facilitated this field, enabling rapid and cost-effective analysis of genetic material. High-throughput sequencing methods, such as sequencing by synthesis and ligation, have revolutionized genomics by allowing the swift sequencing of large DNA quantities, crucial for understanding complex biological systems and diseases.[9.1] Additionally, epigenomic mapping has become essential for understanding gene regulation and expression, offering insights into regulatory mechanisms without altering DNA sequences.[8.1] Bioinformatics tools are crucial for addressing challenges in data integration across different omics disciplines. These tools facilitate the cleaning, normalization, and identification of biomolecules, which are vital for the reliable interpretation of multiomics data.[17.1] By integrating diverse datasets, researchers can achieve a more holistic view of biological processes, guiding the development of novel therapies.[19.1] The combination of metabolomics with other omics data enhances the understanding of cellular function and metabolic changes, underscoring the significance of omics in advancing biological research.[18.1]History
Origins of Omics
The origins of omics can be traced back to the emergence of sequencing techniques in the late 1950s. In 1958, Frederick Sanger developed the first protein sequencing methods, specifically focusing on the amino acid sequence of insulin, marking a significant milestone in the field of molecular biology.[38.1] However, the advancement of sequencing technology remained relatively stagnant for the following two to three decades, limiting the growth of omics disciplines during that period.[38.1] By 2016, the term "omics" had evolved to encompass a wide array of biological disciplines, with researchers identifying over a thousand distinct omics fields.[37.1] These technologies enable the simultaneous measurement of vast numbers of biomolecules, with genomics focusing on DNA sequences and proteomics examining large quantities of proteins.[39.1] The development of various omics technologies has led to the establishment of several sub-disciplines, including transcriptomics, metabolomics, and interactomics, which further expand the scope of biological research.[40.1] The integration of these technologies has significantly contributed to the discovery of new biomarkers and therapeutic targets, thereby accelerating advancements in precision and personalized medicine.[70.1] As omics technologies continue to evolve, they play a crucial role in enhancing our understanding of complex biological systems and improving disease detection and treatment strategies.Evolution of Omics Disciplines
The evolution of omics disciplines has been significantly influenced by advancements in sequencing technologies and bioinformatics tools. The rapid development of high-throughput sequencing and mass spectrometry has led to the emergence of various omics fields, including genomics, transcriptomics, proteomics, and metabolomics, which collectively contribute to a comprehensive understanding of biological systems.[50.1] These technologies have enabled scientists to generate vast amounts of data quickly and cost-effectively, facilitating the integration of different omics fields to explore complex biological questions.[46.1] The integration of DNA and RNA data has improved the ability to match genetic variations with their immediate effects, such as gene fusions or spliced isoforms. Additionally, the use of transcriptome data in proteomics has enhanced analytical capabilities, allowing for the accurate mapping of new proteins and isoforms that were previously unreported.[47.1] This synergy between genomics and transcriptomics exemplifies how advancements in sequencing technologies have fostered a more holistic approach to studying biological processes. Moreover, the application of artificial intelligence (AI) and machine learning in bioinformatics is reshaping the landscape of omics research. These technologies are increasingly being utilized to analyze extensive biological datasets, leading to breakthroughs in personalized medicine, disease diagnostics, and drug development.[63.1] The BMC Methods Collection on "Artificial intelligence for omics data analysis" highlights the potential of AI to leverage multi-omics data, accelerating discoveries in various fields.[64.1] As these technologies continue to evolve, they promise to further enhance our understanding of complex biological systems and improve clinical outcomes.Recent Advancements
High-Throughput Technologies
Recent advancements in omics technologies have greatly enhanced high-throughput capabilities across various biological systems. By integrating genomics, transcriptomics, proteomics, and metabolomics, researchers can now analyze genes, proteins, and metabolites on a large scale, revealing complex mechanisms underlying health and disease.[80.1] This comprehensive systems-level perspective is essential for understanding biological interactions and identifying critical pathways in diseases, particularly in cancer research. Here, multi-omics studies elucidate the interactions between DNA mutations, RNA expression, protein function, and _metabolism.[80.1] The field has also seen significant progress with the advent of highly sensitive, high-throughput, and multiplexed single-cell omics techniques. Innovations in analytical platforms and instrumentation have revitalized omics research, allowing for detailed studies of individual cells and their biological states.[82.1] Single-cell proteomics (SCP), for example, has become prominent due to its ability to profile phenotypic heterogeneity and functional outcomes upon signaling activation, which are often undetectable through other omics approaches.[118.1] This capability provides a more holistic view of cellular behavior and interactions. Furthermore, advancements in mass spectrometry (MS)-based proteomics have significantly advanced single-cell proteomics, enabling the analysis of thousands of single cells per day.[119.1] Despite challenges such as improving sensitivity and throughput, which remain lower compared to single-cell RNA sequencing (scRNA-seq),[119.1] the foundational technologies for single-cell proteomics are well-established. Ongoing research is exploring non-MS-based single molecule protein sequencing approaches to further advance the field.[119.1]Applications in Personalized Medicine
Recent advancements in omics technologies have significantly influenced personalized medicine by integrating multi-omics data and innovative genomic techniques. The rise of high-throughput omics technologies, such as genomics, proteomics, and metabolomics, has shifted the focus from traditional drug discovery methods to comprehensive multi-omics strategies. These strategies enhance the understanding of complex diseases and facilitate the development of tailored therapies.[89.1] A key application of these advancements is in precision medicine, where omics technologies enable treatment customization based on an individual's genetic makeup. For instance, CRISPR technology has emerged as a transformative tool, allowing precise genetic modifications for customized therapies targeting genetic disorders, cancer, and infectious diseases.[97.1] This capability promises to improve health outcomes and enhance the quality of life for patients by aligning treatments with their specific genetic profiles. Moreover, integrating multi-omics data is essential in elucidating disease development mechanisms. Genome-wide association studies (GWAS) have provided insights into the genetic basis of complex diseases, and integrative multi-omics approaches can characterize molecular profiles in relevant primary tissues.[93.1] However, the complexity of biological systems and potential technical artifacts from sample preparation and data analysis pose challenges in ensuring the accuracy and reliability of these analyses.[96.1] To address these challenges, computational methods are being developed to enhance drug discovery efficiency and provide systematic frameworks for investigating drug mechanisms.[85.1] The maturation of omics-based technologies allows for the systematic integration of diverse data outputs, significantly accelerating the drug discovery and development process.[88.1] As the field evolves, ongoing interactions among regulatory agencies, industries, and academia are crucial to overcoming barriers such as the lack of education among scientists and clinicians regarding the efficient use of genomic information in drug development.[87.1]In this section:
Core Disciplines Of Omics
Genomics
Genomics is a fundamental discipline within the broader field of omics, focusing on the comprehensive study of an organism's complete set of DNA, including all of its genes. This discipline plays a crucial role in personalized medicine, as it utilizes genomic data to tailor healthcare strategies to individual genetic profiles. By analyzing the genetic variations present in each person's genome, genomics can reveal hidden causes of diseases, predict the risk of developing certain conditions, and inform the efficacy of specific treatments before they are administered.[164.1] The integration of genomic data with other omics disciplines, such as proteomics and metabolomics, enhances our understanding of complex diseases. This multi-omics approach allows researchers to explore the intricate molecular underpinnings of health and disease, facilitating the identification of comprehensive biomarker sets associated with various health conditions.[142.1] Furthermore, the combination of genomic insights with advanced technologies, such as CRISPR and next-generation sequencing, is expected to revolutionize the field of personalized medicine by enabling more precise and effective treatment strategies.[163.1] As genomic medicine continues to evolve, its integration into routine clinical practice is becoming increasingly prominent. Initiatives such as embedding whole genome sequencing into healthcare systems aim to mainstream genetic testing, thereby enhancing the ability of clinicians to provide personalized interventions and treatments.[165.1] This shift underscores the growing importance of genomics in understanding disease mechanisms and improving patient outcomes through tailored healthcare solutions.Proteomics
Proteomics is a core discipline within the field of omics that focuses on the large-scale study of proteins, particularly their functions and structures. It plays a crucial role in understanding the proteome, which is defined as the entire set of proteins expressed in a cell, tissue, or organism. This understanding is essential for unraveling cellular processes, elucidating disease mechanisms, and advancing drug development.[153.1] Over the past two decades, proteomics has evolved significantly, driven by technological innovations and computational advancements.[153.1] Mass spectrometry (MS) has emerged as a pivotal technology in proteomics, enabling the identification of protein-protein interactions (PPIs) and providing insights into the dynamics of biological systems.[143.1] Recent advancements in MS-based techniques, such as affinity purification, proximity labeling, and cross-linking, have enhanced the ability to study the interactome, which is the complete set of molecular interactions in a biological system.[144.1] These techniques allow researchers to map PPIs and understand their roles in various biological activities, including signal transduction and transcription control, which are critical for maintaining cellular functions.[145.1] The integration of proteomics with other omics disciplines, such as genomics and metabolomics, is also noteworthy. This interdisciplinary approach facilitates a more comprehensive understanding of biological systems and disease mechanisms.[152.1] For instance, the application of proteomics in cancer research has revealed how mutations in DNA can influence protein function and metabolism, thereby driving tumor growth and metastasis.[124.1] Furthermore, proteomics technologies, including protein microarrays and bioinformatics, have been instrumental in biomarker discovery, enhancing the potential for therapeutic targets in various diseases.[154.1]In this section:
Applications Of Omics
In Medicine
Omics technologies have significantly advanced the field of medicine, particularly in understanding diseases and developing personalized treatment strategies. By analyzing genes, proteins, and metabolites on a large scale, omics provides a systems-level view that allows researchers to uncover intricate mechanisms underlying health and disease, thereby personalizing treatments and addressing environmental challenges.[168.1] The integration of various omics disciplines, including genomics, proteomics, and metabolomics, has been pivotal in cancer research, where multi-omics studies reveal how mutations in DNA interact with changes in RNA expression, protein function, and metabolism to drive tumor growth and metastasis.[168.1] In the context of cancer, initiatives such as The Cancer Genome Atlas (TCGA) have identified numerous cancer-associated mutations, enabling the classification of tumors based on their genetic profiles and paving the way for precision medicine.[169.1] For instance, the PARP inhibitor Olaparib has been tested in patients with advanced breast or ovarian cancer harboring BRCA mutations, demonstrating the potential of targeted therapies informed by genomic data.[169.1] Furthermore, the integration of omics data can accelerate drug discovery and development by providing in-depth molecular insights that help predict drug efficacy and safety more efficiently.[176.1] The regulatory landscape for drug approval is also evolving due to the integration of omics technologies. Regulatory bodies can enhance the efficiency of the drug approval process by utilizing analytic tools, machine learning, and big data, which can help cut costs while adhering to rigorous regulatory measures.[175.1] However, challenges remain, including high costs and long timelines for drug candidates to receive regulatory approval, as well as low success rates in clinical trials.[177.1] The complexity of disease biology, particularly in cancer, necessitates innovative approaches, such as AI-driven frameworks like PaccMann, which integrates biological data for drug identification.[177.1] Moreover, the role of proteomics in understanding complex diseases is becoming increasingly important. By studying protein structures, functions, and interactions, researchers can gain insights into disease mechanisms at the molecular level.[181.1] Techniques such as mass spectrometry and single-cell proteomics allow for detailed analysis of protein expression and interactions, which are crucial for developing personalized treatment approaches.[182.1] The integration of proteomics with other omics technologies enhances our understanding of complex diseases and facilitates the identification of relevant diagnostic and predictive biomarkers, ultimately leading to more effective treatment plans for patients.[180.1]Challenges And Future Directions
Data Complexity and Standardization
The complexity of omics data presents significant challenges in the field of biological research and clinical applications. Omics technologies, which encompass genomics, transcriptomics, proteomics, and metabolomics, generate vast amounts of data that require advanced computational infrastructure and bioinformatics expertise for effective analysis.[206.1] The integration of multi-omics data is particularly transformative, as it enhances our understanding of complex diseases and drives advancements in precision medicine.[213.1] However, this integration is complicated by the heterogeneous nature of the data, necessitating sophisticated analytical methods to extract meaningful biological insights.[208.1] A critical issue in the analysis of omics data is the lack of standardization in data collection and processing across different studies. This inconsistency limits the reproducibility and comparability of results, which are essential for validating findings and translating them into clinical practice.[214.1] Furthermore, the regulatory frameworks governing omics-based tests differ from those for traditional medical technologies, complicating the establishment of standardized protocols.[215.1] The complexity of defining biological rationales behind tests that utilize multiple biomarkers further exacerbates these challenges.[214.1] To address these issues, there is a pressing need for the development of standardized methodologies and regulatory measures that can enhance the reproducibility and reliability of omics data. This includes establishing common protocols for data collection, processing, and sharing, as well as fostering collaboration among researchers to facilitate data integration and interpretation.[215.1] By overcoming these challenges, the field of omics can better harness its potential to revolutionize personalized medicine and improve patient outcomes.In this section:
Multi-Omics Approaches
Integration of Different Omics
The integration of different omics is central to multi-omics research, which synthesizes diverse biological datasets to deepen our understanding of complex biological systems. This approach encompasses genomics, transcriptomics, proteomics, metabolomics, and microbiomics, enabling simultaneous analysis of multiple biological information layers.[251.1] Such integration is crucial for providing a holistic view of health and disease by exploring the molecular intricacies underlying various biological phenomena.[253.1] Effective data integration strategies are vital for merging the complementary insights from each omics layer. These strategies include early integration, where datasets are combined into a single matrix, and mixed integration, which involves transforming each dataset independently before merging them for analysis.[254.1] These methodologies allow for the correlation of different biological signals, such as linking mRNA levels with protein abundance, to elucidate RNA molecules' functional roles and regulatory mechanisms.[250.1] Machine learning techniques are pivotal in multi-omics data integration, facilitating the creation of predictive models that can identify risk factors before clinical symptoms appear. These models improve clinical outcome predictions by integrating data from various omics and clinical sources.[258.1] Ensemble machine learning algorithms, particularly effective in late integration strategies, combine predictions from different datasets to enhance stability and predictive accuracy.[256.1] However, late integration may not fully capture inter-omics interactions, potentially limiting biological insights.[257.1] In precision medicine, multi-omics integration aids in patient stratification and personalizing treatment strategies. By leveraging integrated data, healthcare providers can make informed decisions regarding experimental design and data analysis pipelines, enhancing the reliability and interpretability of biological insights.[258.1] This transformative potential is expected to revolutionize clinical practices by tailoring interventions to individual patients' unique molecular characteristics.[258.1]Case Studies of Multi-Omics Applications
Multi-omics approaches have been instrumental in advancing our understanding of various diseases through comprehensive data integration and analysis. One notable application is in the field of cancer research, where multi-omics datasets have been utilized to classify cancer subtypes effectively. For example, a study demonstrated that distinct gene expression and mutation profiles could be identified in adult patients with acute myeloid leukemia (AML) through multi-omics analysis, revealing unique gene expression and methylation patterns across samples.[275.1] Similarly, multi-omics profiling of pancreatic ductal adenocarcinoma (PDAC) integrated data from 150 patients, encompassing mutations, gene expression (including mRNA, miRNA, and long non-coding RNA), DNA methylation, and protein expression, thereby enhancing the understanding of this complex disease.[275.1] In cardiovascular research, the Framingham Heart Study has incorporated multi-omics approaches since its inception in 1948, utilizing genomics, proteomics, and metabolomics data to unravel the intricate genetic and molecular mechanisms underlying cardiovascular diseases.[274.1] This integration of multi-omics data is expected to drive innovations in disease prevention, diagnosis, and treatment, ultimately leading to more personalized healthcare solutions. Moreover, multi-omics approaches are proving to be pivotal in managing atherosclerotic cardiovascular disease (ASCVD). By leveraging artificial intelligence (AI) and machine learning (ML) models, researchers can integrate multi-omics and clinical data to develop advanced tools for accurate ASCVD risk prediction. These strategies offer promising preventive and therapeutic options that extend beyond traditional risk factors.[276.1]In this section:
References
[3] Omics | Description, Fields, & Applications | Britannica — omics, any of several areas of biological study defined by the investigation of the entire complement of a specific type of biomolecule or the totality of a molecular process within an organism. In biology the word omics refers to the sum of constituents within a cell. The omics sciences share the overarching aim of identifying, describing, and quantifying the biomolecules and molecular
[4] What are omics sciences? - Illinois Experts — The word omics refers to a field of study in biological sciences that ends with -omics, such as genomics, transcriptomics, proteomics, or metabolomics. The ending -ome is used to address the objects of study of such fields, such as the genome, proteome, transcriptome, or metabolome, respectively. More specifically genomics is the science that studies the structure, function, evolution, and
[5] What is omics data? - California Learning Resource Network — Omics data has numerous applications across various fields, including: Biology: Omics data is used to understand the structure, function, and evolution of genomes, proteins, and metabolites, leading to insights into disease mechanisms and potential therapeutic targets.
[8] Advancements in DNA Sequencing and Genomic Analysis Techniques — Advancements in DNA Sequencing and Genomic Analysis Techniques - BiologyInsights Advancements in DNA Sequencing and Genomic Analysis Techniques Explore the latest innovations in DNA sequencing and genomic analysis, enhancing our understanding of genetics and personalized medicine. DNA sequencing and genomic analysis have advanced significantly, enhancing our understanding of genetics and its applications in medicine and biology. This article explores key developments in DNA sequencing and genomic analysis. Epigenomic mapping is transforming our understanding of how genes are regulated and expressed without altering the underlying DNA sequence. Metagenomics tools, such as shotgun sequencing and bioinformatics pipelines like QIIME and MG-RAST, facilitate the analysis and interpretation of complex microbial data, driving innovation and understanding in both health and environmental sciences.
[9] Advancements in Genetic Sequencing Techniques and Genomic Research — Advancements in Genetic Sequencing Techniques and Genomic Research - BiologyInsights Advancements in Genetic Sequencing Techniques and Genomic Research Explore the latest advancements in genetic sequencing and their impact on genomic research and innovation. Decoding DNA sequences enables scientists to explore genetic variations, understand diseases, and develop targeted therapies. Genetic Sequencing Techniques This method, faster than Sanger sequencing, is useful for sequencing short DNA fragments and has been applied in microbial genomics and mutation detection. In whole-genome sequencing, every nucleotide in an organism’s DNA can be identified, providing a comprehensive view of its genetic makeup. Sequence alignment algorithms are essential tools in genomic research, providing the framework for comparing DNA, RNA, or protein sequences.
[13] Unlocking the Future of Precision Medicine: The Importance of ... — Multiomics refers to the combined study of multiple 'omics' disciplines, such as genomics, transcriptomics, proteomics, metabolomics, and epigenomics. By analyzing these layers of biological information simultaneously, researchers can gain a more comprehensive understanding of disease mechanisms, biomarker discovery, and individual
[17] Special Issue: "Bioinformatics and Omics Tools" - PMC — In response to these challenges, terms such as integrated omics, multi-omics, poly-omics, trans-omics, and pan-omics have emerged, all referring to the integration of different omics datasets. In the integration of omics data, one faces a variety of obstacles, including data cleaning, normalization, biomolecule identification, data
[18] Metabolomics in Multiomics Research: 5 Key Case Studies of Multiomics ... — By integrating data from genomics, transcriptomics, proteomics, metabolomics, and other omics disciplines, researchers can unravel intricate interactions and pathways within cells and organisms. ... providing a snapshot of cellular function and metabolic changes. Combining metabolomics with other omics data enhances understanding of biological
[19] Multi-omics integration in biomedical research - A metabolomics-centric ... — This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Therefore, combining omics data from multiple biological domains (e.g., levels of transcripts, proteins, or metabolites) in multi-omics studies is a promising approach towards a more detailed molecular understanding of health and disease, as well as the chain of cause and effect, which is an essential requirement for guiding novel therapies . In this review, we will provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with more than two omics and highlighting their application in recent multi-omics studies. Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data
[37] History of Omics - Omics.org — The History of Omics: as a generic name for various omics and a standalone biology disciplines. Jong Bhak. Korean Genomics Center , UNIST, Ulsan, South Korea. jongbhak@genomics.org. Abstract. There are perhaps over a thousand omics fields in biology by 2016. Researchers are organizing the life science disciplines to be more efficient and easy
[38] Introduction to Multi-Omics - SpringerLink — The omics studies have quite a long history. Back in 1958, the first sequencing technique emerged, as Frederick Sanger has invented the protein sequencing methods, especially the amino acid sequence of insulin (Heather and Chain 2016).However, sequencing technology did not develop significantly during the next twenty to thirty years.
[39] Evolution of Translational Omics - NCBI Bookshelf - National Center for ... — Technologies collectively called omics enable simultaneous measurement of an enormous number of biomolecules; for example, genomics investigates thousands of DNA sequences, and proteomics examines large numbers of proteins. Scientists are using these technologies to develop innovative tests to detect disease and to predict a patient's likelihood of responding to specific drugs. Following a
[40] Advances and Trends in Omics Technology Development - PMC — While sequencing-based approaches are feasible for studies on genome, transcriptome, their epitomes and interactomes involving DNA/RNA, MS-based techniques can be used to interrogate proteome, metabolome, and interactomes that do not involve DNA/RNA (Figure 1, Table 1). | Single cell RNA sequencing | CEL-seq2 | • High sensitivities; | • The informative sequence lengths on the RNA side and the DNA side are both limited to 20 bases, resulting in challenges in unambiguous sequence mapping. Genomics, transcriptomics, proteomics and metabolomics, namely the “four big omics” (240), have led to the creation of their epiomics (epigenomics, epitranscriptomics, and epiproteomics) and interactomics (e.g., DNA-RNA interactomics, RNA-RNA interactomics, DNA-protein interactomics, RNA-protein interactomics, protein-protein interactomics, and protein-metabolite interactomics) which are technology-based, and other omics such as immunomics and microbiomics that are knowledge-based.
[46] Integrative omics for health and disease - Nature — Advances in omics technologies — such as genomics, transcriptomics, proteomics and metabolomics — have begun to enable personalized medicine at an extraordinarily detailed molecular level.
[47] Genome, transcriptome and proteome: the rise of omics data and their ... — In the former case, integration of DNA and RNA data has led to an improvement in matching genetic variations with their immediate effect, e.g. gene fusion or spliced isoforms ; in the latter, the use of transcriptome data in proteomics has increased the analytical power when transcriptome data was used to determine the mRNA content in a sample that had subsequently undergone proteome profiling and helped in accurately mapping new proteins and isoforms not reported in reference databases .
[50] Advances and Trends in Omics Technology Development — The human history has witnessed the rapid development of technologies such as high-throughput sequencing and mass spectrometry that led to the concept of "omics" and methodological advancement
[63] Machine learning meets omics: applications and perspectives — Specifically, we describe how artificial intelligence can be applied to omics studies and review recent advancements at the interface between machine learning and the ever-widest range of omics including genomics, transcriptomics, proteomics, metabolomics, radiomics, as well as those at the single-cell resolution.
[64] Artificial intelligence for omics data analysis - BMC Methods — The BMC Methods Collection "Artificial intelligence for omics data analysis" will feature novel artificial intelligence approaches leveraging multi-omics data to accelerate discoveries in personalized medicine, disease diagnostics, drug development, and biological pathway elucidation. Acknowledging the importance of this field, the BMC Methods Collection “Artificial intelligence for omics data analysis” (https://www.biomedcentral.com/collections/aioda), focuses on publishing innovative AI approaches using multi-omics data to accelerate discoveries in areas like personalized medicine, disease diagnostics, drug development, and biological pathway elucidation. Dr Ahmed’s lab at Rutgers is focused on implementing Artificial Intelligence (AI), Machine Learning (ML), and standard bioinformatics approaches to multi-omics/genomic and phenotypic data for the identification of patterns revealing predictive biomarkers and risk factors to support earlier diagnosis of patients with complex traits.
[70] The omics technologies and liquid biopsies: Advantages, limitations ... — In the recent years, the development of so-called omics technologies has greatly contributed to the discovery of new biomarkers and targets, spanning different areas from diagnosis to therapy, and helping to accelerate the progress of precision and personalized medicine.
[80] Omics Explained: A Comprehensive Overview - SilicoGene — By analyzing genes, proteins, and metabolites at a large scale, omics enables scientists to uncover intricate mechanisms underlying health and disease, personalize treatments, and address environmental challenges. Omics technologies provide a systems-level view, allowing researchers to dissect these complexities and identify critical pathways driving disease. In cancer research, multi-omics studies are uncovering how mutations in DNA interact with changes in RNA expression, protein function, and metabolism to drive tumor growth and metastasis. Omics refers to the large-scale study of biological molecules, such as genes, proteins, and metabolites, to understand how they interact within a system. Genomics focuses specifically on the study of DNA, while multi-omics integrates data from multiple fields, such as transcriptomics (RNA), proteomics (proteins), and metabolomics (metabolites).
[82] Advancements and applications of single-cell multi-omics techniques in ... — Recent advancements in analytical platforms and instrumentation have revitalized this field, paving the way for the emergence of highly sensitive, high-throughput, and multiplexed single-cell omics techniques. ... Zou F., Yousef M. Invention of 3Mint for feature grouping and scoring in multi-omics. Front. Genet. 2023;14 doi: 10.3389/fgene.2023.
[85] A Review of Latest Tools and Technologies in Pharmaceutical Research — The study highlights how computational methods enhance drug discovery efficiency, while omics technologies provide systematic frameworks for investigating drug mechanisms. The integration of these
[87] The integration of emerging omics approaches to advance precision ... — Precision medicine using omics technologies and approaches presents many unique challenges to drug and medical device regulation, and the FDA has referenced this in more than one of their regulatory science priority areas within their strategic plan .
[88] "Omics"-Informed Drug and Biomarker Discovery: Opportunities ... — The maturation of Omics-based technologies has meant that resulting data outputs, if systematically integrated, could have a significant impact on accelerating the drug discovery and development process by addressing some of the challenges mentioned above. The drug development pathway for a small molecule entails an exhaustive process which includes basic research, target identification and validation, lead generation and optimisation, pre-clinical testing, phased-clinical trials in humans and regulatory approval by the FDA (Figure 1). The key to future drug discovery will reside in our ability to harness the powerful new technologies already at our disposal to integrate information from sequenced genomes, functional genomics, protein profiling, metabolomics and bioinformatics, in a manner that ensures a comprehensive systems-based analysis to further our understanding of the complexities of health and disease.
[89] Recent updates and challenges on the regulation of precision medicine ... — The rapid progress in "omics", such as genomics, metabolomics, microbiomics, has paved the path for precision medicine and revolutionized the development of drugs and devices promising to meet unmet medical needs. The aim of the present study was to investigate the current regulatory framework established by the United States Food and Drug Administration (USFDA) and to identify challenges
[93] Insights from multi-omics integration in complex disease primary ... — Genome-wide association studies (GWAS) have provided insights into the genetic basis of complex diseases. In the next step, integrative multi-omics approaches can characterize molecular profiles in relevant primary tissues to reveal the mechanisms that underlie disease development. Here, we highlight recent progress in four examples of complex diseases generated by integrative studies: type 2
[96] Multiomic Big Data Analysis Challenges: Increasing Confidence in the ... — The need for holistic molecular measurements to better understand disease initiation, development, diagnosis, and therapy has led to an increasing number of multiomic analyses. The wealth of information available from multiomic assessments, however, requires both the evaluation and interpretation of extremely large data sets, limiting analysis throughput and ease of adoption. Computational
[97] CRISPR and Gene Editing: The Future of Personalized Medicine — CRISPR technology is at the forefront of this transformation, enabling the development of customized treatments based on a patient’s unique genetic makeup. One of the most promising applications of CRISPR in personalized medicine is in the treatment of genetic disorders. In conclusion, CRISPR represents a transformative tool in the field of personalized medicine, with the potential to revolutionize the treatment of genetic disorders, cancer, infectious diseases, and beyond. By enabling precise genetic modifications, CRISPR paves the way for customized therapies that align with the unique genetic profiles of individual patients, heralding a new era in medical treatment that promises to improve health outcomes and enhance quality of life.
[118] Recent advances in microfluidics for single-cell functional proteomics ... — Single-cell proteomics (SCP) reveals phenotypic heterogeneity by profiling individual cells, their biological states and functional outcomes upon signaling activation that can hardly be probed via other omics characterizations. This has become appealing to researchers as it enables an overall more holistic v Lab on a Chip Review Articles 2023
[119] Trends and Advancements in Proteomics | Technology Networks — But MS-based single-cell proteomics is not without its limitations; its sensitivity could still be improved, and throughput is still much lower than scRNA-seq, albeit recent methods have enabled analysis of thousands of single cells per day. In Nature Methods, Mann, alongside Dr. Florian Rosenberger and Dr. Marvin Thielert, argues that, “while most of the building blocks for single-cell proteomics are in place, applications to date have mostly been technological proofs of principle.” In the authors’ opinion, this is largely due to the “limited depth of quantitative depth of coverage achieved so far” and issues relating to throughput. Looking to the future of the proteomics field, a growing area of interest has been building around non-MS-based single molecule protein sequencing approaches, which measure individual copies of peptides.
[124] Omics Explained: A Comprehensive Overview - SilicoGene — By analyzing genes, proteins, and metabolites at a large scale, omics enables scientists to uncover intricate mechanisms underlying health and disease, personalize treatments, and address environmental challenges. Omics technologies provide a systems-level view, allowing researchers to dissect these complexities and identify critical pathways driving disease. In cancer research, multi-omics studies are uncovering how mutations in DNA interact with changes in RNA expression, protein function, and metabolism to drive tumor growth and metastasis. Omics refers to the large-scale study of biological molecules, such as genes, proteins, and metabolites, to understand how they interact within a system. Genomics focuses specifically on the study of DNA, while multi-omics integrates data from multiple fields, such as transcriptomics (RNA), proteomics (proteins), and metabolomics (metabolites).
[142] Multi-omics integration in biomedical research - A metabolomics-centric ... — This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Therefore, combining omics data from multiple biological domains (e.g., levels of transcripts, proteins, or metabolites) in multi-omics studies is a promising approach towards a more detailed molecular understanding of health and disease, as well as the chain of cause and effect, which is an essential requirement for guiding novel therapies . In this review, we will provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with more than two omics and highlighting their application in recent multi-omics studies. Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data
[143] Characterizing Protein-Protein Interactions Using Mass Spectrometry ... — During the past decades, mass spectrometry (MS)-based proteomics has become an important technology to identify protein-protein interactions (PPIs). The application of a quantitative filter in protein enrichments from crude lysates to discriminate bona fide interactors from background proteins has proved to be particularly powerful. Recently
[144] Recent Advances in Mass Spectrometry-Based Protein Interactome Studies — Recent Advances in Mass Spectrometry-based Protein Interactome Studies - ScienceDirect Recent Advances in Mass Spectrometry-based Protein Interactome Studies Recent developments in mass spectrometry (MS)-based techniques, including affinity purification, proximity labeling, cross-linking, and co-fractionation mass spectrometry (MS), have significantly enhanced our abilities to study the interactome. Finally, we highlight state-of-the-art bioinformatic approaches for predictions of interactome and complex modeling, as well as strategies for combining experimental interactome data with computation methods, thereby enhancing the ability of MS-based techniques to identify protein interactomes. This review highlights recent advancements in mass spectrometry-based techniques for mapping protein interactomes, including affinity purification, proximity labeling, cross-linking, and co-fractionation approaches.
[145] Mapping protein-protein interactions by mass spectrometry. — Protein-protein interactions (PPIs) are essential for numerous biological activities, including signal transduction, transcription control, and metabolism. They play a pivotal role in the organization and function of the proteome, and their perturbation is associated with various diseases, such as cancer, neurodegeneration, and infectious diseases.
[152] PDF — understanding disease dynamics . The future of proteomics looks promising with ongoing advancements in technology and methodology. Integration of proteomics with other omics disciplines, such as genomics and metabolomics, will provide a more comprehensive understanding of biological systems. Artificial intelligence
[153] Advancements in Proteomics: From Protein Identification to Functional ... — Understanding the proteome—the entire set of proteins expressed in a cell, tissue, or organism—is critical for unraveling cellular processes, elucidating disease mechanisms, and advancing drug development. Over the past two decades, proteomics has evolved dramatically, driven by technological innovations and computational advancements.
[154] Proteomics Discovery of Disease Biomarkers - PMC — Examples of two promising proteomics technologies are mass spectrometry, including an instrument based on surface enhanced laser desorption/ionization, and protein microarrays. This review describes mass spectrometry, protein microarrays, and bioinformatics and their roles in biomarker discovery, and highlights the significance of integration between proteomics and bioinformatics. This method was utilized in order to identify proteins shed from the extracellular surface of hamster cells using mouse and human protein databases with limited false-positive assignments (Ahram et al. For example, differential protein expression in microdissected prostate cancer cells has been compared to that in patient-matched normal and premalignant cells from the same tissue samples (Paweletz et al. In a recent study, a human proteome database was analyzed using five predictive computational methods in search for membrane proteins (Ahram et al.
[163] The Future of Personalized Medicine and Genomic Medicine: A 20-Year ... — The fields of personalized medicine and genomic medicine are at the forefront of a healthcare revolution. By leveraging genomic insights, advanced diagnostics, and tailored therapies, these disciplines are transforming how we prevent, diagnose, and treat diseases. Over the next two decades, the integration of cutting-edge technologies such as liquid biopsies, AI-driven genomics, and CRISPR
[164] Genomic Medicine and the Future of Health Care — Personalized medicine, also called genomic medicine, uses information encoded within each person’s genome—our complete set of DNA instructions—to tailor their health care. A DNA-based approach to health can reveal a hidden cause of disease, predict the risk of developing disease years before it would happen and, in some cases, suggest whether a particular drug might work before it is prescribed. In large part, this is due to the strength of our research program and the unique resources built here, including the Utah Population Database, the world’s largest genealogical database linked to medical records. One of the most exciting recent advances is the integration of personalized medicine practices into clinical care.
[165] The rise of the genome and personalised medicine - PMC — As set out in the Annual report of the Chief Medical Officer 2016: Generation Genome_1 and the recent NHS England board paper _Creating a genomic medicine service to lay the foundations to deliver personalised interventions and treatments,2 the increasing ‘mainstreaming’ of genetic testing into routine practice and plans to embed whole genome sequencing in the NHS mean that the profile and importance of genomics is on the rise for many clinicians. Every human genome contains around 3–5 million genetic variants compared with the reference sequence. Genomic medicine has the capacity to revolutionise the healthcare of an individual with a rare disease or cancer by offering prompt and accurate diagnosis, risk stratification based upon genotype and the capacity for personalised treatments.
[168] Omics Explained: A Comprehensive Overview - SilicoGene — By analyzing genes, proteins, and metabolites at a large scale, omics enables scientists to uncover intricate mechanisms underlying health and disease, personalize treatments, and address environmental challenges. Omics technologies provide a systems-level view, allowing researchers to dissect these complexities and identify critical pathways driving disease. In cancer research, multi-omics studies are uncovering how mutations in DNA interact with changes in RNA expression, protein function, and metabolism to drive tumor growth and metastasis. Omics refers to the large-scale study of biological molecules, such as genes, proteins, and metabolites, to understand how they interact within a system. Genomics focuses specifically on the study of DNA, while multi-omics integrates data from multiple fields, such as transcriptomics (RNA), proteomics (proteins), and metabolomics (metabolites).
[169] Applications of Omics Technologies in Medicine — We reviewed studies on the applications of omics technologies in understanding disease and treatment, with key sections on genomics in cancer research and epidemiology. Initiatives such as The Cancer Genome Atlas (TCGA) have identified numerous cancer-associated mutations, enabling the classification of tumors based on their genetic profiles and paving the way for precision medicine (National Cancer Institute, n.d.). Olaparib, an orally active PARP inhibitor, was tested in phase II studies led by Andrew Tutt and William Audeh on patients with advanced breast or ovarian cancer harboring BRCA mutations (Audeh et al., 2010; Tutt et al., 2010). Figure 3: The prevalence of epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer (NSCLC) patients based on the study by Sousa et al.
[175] PDF — Through integrated modern technological elements such as analytic tools, machine learning, and big data, regulatory bodies and entities in the pharmaceutical industry can significantly enhance the efficiency of the drug approval process while cutting costs and conforming to rigorous regulatory measures.
[176] Multi-Omics Data Integration in Drug Discovery - PharmaLex — Accelerate drug discovery and development: Omics data, when integrated, can provide in-depth molecular insights that can help businesses save time and resources in drug discovery research and predicting drug efficacy and safety at a quicker pace.
[177] Integrating artificial intelligence in drug discovery and early drug ... — There are several limitations, specific to drug discovery and development in cancer, that can be summarized in the following concepts: (1) High Costs and Long Timelines: 10–15 years for a drug candidate to receive regulatory approval ; (2) Low Success Rates: approximately 90% of candidates that enter early clinical trials do not reach the market ; and (3) Complex Disease Biology: cancer involves complex, interconnected biological pathways that are difficult to target effectively with classical methods. As the main reasons for failures in drug development are insufficient efficacy and safety levels, methods based on AI could help mitigate challenges in the analysis of multiomics data by improving target identification and predicting druggability, which enhances the overall drug discovery process. An example of the integration of biological data for drug identification is PaccMann, an AI-driven framework designed to predict cancer cell sensitivity to compounds by integrating molecular structures, gene expression profiles, and protein interaction data.
[180] Unveiling the future of metabolic medicine: omics technologies driving ... — Unveiling the future of metabolic medicine: omics technologies driving personalized solutions for precision treatment of metabolic disorders - ScienceDirect Unveiling the future of metabolic medicine: omics technologies driving personalized solutions for precision treatment of metabolic disorders By analyzing a patient's metabolomic, proteomic, genetic profile, and clinical data, physicians can identify relevant diagnostic, and predictive biomarkers and develop treatment plans and therapy for acute and chronic metabolic diseases. This review article aims to explore the potential of personalized medicine utilizing omics approaches for the treatment of metabolic disorders. Personalized medicine is a promising strategy for treating metabolic disorders, and omics technologies, including genomics, proteomics, metabolomics, and other related fields, are crucial in its development (Table 1).
[181] The Role of Proteomics in Understanding Diseases — Proteomics, the large-scale study of proteins, is transforming the way we understand diseases at the molecular level. By analyzing protein structures, functions, and interactions, researchers can
[182] Understanding proteomics: Techniques and applications - Abcam — Proteomics is the large-scale study of proteins, particularly their functions and interactions within a biological system. These techniques enable researchers to explore the complex proteome and gain insights into protein functions, structures, and interactions. Single-cell proteomics is a method that studies protein expression in individual cells, revealing insights into cellular diversity, development, and disease progression. Proteomics investigates protein expression, activity, and interactions. Proteomics techniques include mass spectrometry, which identifies and quantifies proteins; two-dimensional gel electrophoresis (2DGE), which separates proteins based on their size and charge; shotgun proteomics, a bottom-up approach for identifying proteins in complex mixtures using high-performance liquid chromatography and mass spectrometry, which analyzes complex mixtures; affinity-based methods, such as co-immunoprecipitation, to study protein interactions; and the use of protein microarrays for high-throughput analysis of protein interactions and activities.
[206] Integrative Omics Approaches in Cardiovascular Disease Research ... — Challenges and Future Directions. Despite the immense potential of omics technologies, several challenges remain. Large-scale omics data sets require advanced computational infrastructure and expertise in bioinformatics for effective analysis. The lack of standardisation in data collection and processing across studies also limits the
[208] Tensor-Based Approaches for Omics Data Analysis: Applications ... — Challenges and future directions for tensor-based omics data analysis are also discussed, emphasizing the potential of these methods to extract meaningful biological insights from complex, heterogeneous datasets and advance our understanding of biological systems. ... The unprecedented influx of multidimensional, and complex omics data presents
[213] Emerging Trends in Multi-Omics Data Integration: Challenges and Future ... — Zhang J., 2024, Emerging Trends in multi-omics data integration: challenges and future directions, Computational Molecular Biology, 14(2): 64-75 (doi: 10.5376/cmb.2024.14.0008) Similarly, in neurodegenerative diseases, multi-omics data integration has shed light on the molecular mechanisms underlying disease onset and progression, paving the way for the development of novel diagnostic and therapeutic approaches (Reel et al., 2021; Terranova and Venkatakrishnan, 2023).The integration of multi-omics data is transforming our understanding of complex diseases and driving the advancement of precision medicine. This integration of cloud computing and big data analytics is essential for overcoming the challenges associated with multi-omics data, paving the way for new discoveries in systems biology and precision medicine (Koppad et al., 2021; Kang et al., 2021).
[214] Summary | Evolution of Translational Omics: Lessons Learned and the ... — individual biomarkers, the complexity of data sharing with other scientists, and the high degree of hope placed in the promise of omics-enabled technologies and medical care. Omics-based tests, and indeed all clinical laboratory tests, are subject to a different regulatory framework than drugs.
[215] Summary - Evolution of Translational Omics - NCBI Bookshelf — Several characteristics distinguish omics-based tests from other medical technologies, including a different regulatory oversight process, the difficulty in defining the biological rationale behind a test based on multiple individual biomarkers, the complexity of data sharing with other scientists, and the high degree of hope placed in the
[250] Multiomics Research: Principles and Challenges in Integrated Analysis — Multiomics research is a transformative approach in the biological sciences that integrates data from genomics, transcriptomics, proteomics, metabolomics, and other omics technologies to provide a comprehensive understanding of biological systems. When integrated with transcriptome sequencing data obtained from the same biological sample, this methodology facilitates comprehensive analysis of miRNA expression alongside its target genes, thereby offering a robust investigative tool for elucidating the functional roles and regulatory mechanisms of RNA molecules. | Data integration | Combining data from multiple omics platforms (e.g., genomics, transcriptomics, and proteomics) to obtain a comprehensive view of biological systems | Integrating RNA-seq gene expression data with proteomics data to correlate mRNA levels with protein abundance |
[251] Introduction to Multiomics Technology | SpringerLink — Multiomics is a biological analysis approach that utilizes data from multiple sets. The word omics refers to the different types of omes including genome, epigenome, proteome, transcriptome, metabolome, and microbiome. ... 7 Summary. Multiomics is an exciting new field of research that integrates multiple layers of information, including
[253] Multi-omics integration in biomedical research - ScienceDirect — This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Therefore, combining omics data from multiple biological domains (e.g., levels of transcripts, proteins, or metabolites) in multi-omics studies is a promising approach towards a more detailed molecular understanding of health and disease, as well as the chain of cause and effect, which is an essential requirement for guiding novel therapies . In this review, we will provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with more than two omics and highlighting their application in recent multi-omics studies. Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data
[254] Integration strategies of multi-omics data for machine learning ... — Integration strategies of multi-omics data for machine learning analysis - ScienceDirect Integration strategies of multi-omics data for machine learning analysis Multi-omics data integration strategies are needed to combine the complementary knowledge brought by each omics layer. In this mini-review, we focus on challenges and existing multi-omics integration strategies by paying special attention to machine learning applications. Schematic representation of the main strategies for multi-omics datasets integration. A) Early integration concatenates all omics datasets into a single matrix on which machine learning model can be applied. B) Mixed integration first independently transforms or maps each omics block into a new representation before combining them for downstream analysis. Next article in issue For all open access content, the relevant licensing terms apply.
[256] Benchmarking ensemble machine learning algorithms for multi-class ... — Benchmarking ensemble machine learning algorithms for multi-class, multi-omics data integration in clinical outcome prediction - PubMed Benchmarking ensemble machine learning algorithms for multi-class, multi-omics data integration in clinical outcome prediction Benchmarking ensemble machine learning algorithms for multi-class, multi-omics data integration in clinical outcome prediction In this work, we compare the performance of a variety of ensemble machine learning (ML) algorithms that are capable of late integration of multi-class data from different modalities. Our work shows that integrating complementary omics and data modalities with effective ensemble ML models enhances accuracy in multi-class clinical outcome predictions and produces more stable predictive features than individual modalities or simple concatenation. Keywords: cancer; clinical outcome prediction; hepatocellular carcinoma; late integration; machine learning; multi-class; multi-modal; multi-omics.
[257] Multiomics, artificial intelligence, and precision medicine in ... — Using machine learning tools and algorithms, it is possible to integrate multiomics data with clinical information to develop predictive models that identify risk before the condition is clinically apparent, thus facilitating early interventions to improve the health trajectories of the patients. However, the downside of the late integration strategy is that it cannot capture inter-omics interactions as the different ML models (for the different omics data sets) do not share complementary information between omics.28 Combining predictions as in the late integration strategy may not fully bring out the details and complexity of multiomics data analysis and interpretation to understand the biological mechanisms of diseases.
[258] Mastering Multi-omics Integration: Theory, Methods, and Applications — It allows for informed decisions regarding experimental design, data analysis pipelines, and the integration of multi-omics datasets, ultimately enhancing the reliability and interpretability of biological insights. Integrative predictive modeling involves building comprehensive models that capture the complexity of multi-omics data, allowing for the prediction of relevant outcomes or understanding complex biological relationships. Here are key components and techniques for integrative predictive modeling in the context of multi-omics data: Model interpretation and biological validation are integral components of the multi-omics data analysis pipeline. Train machine learning models using integrated multi-omics data to predict individual responses to specific treatments. Implementing precision medicine and patient stratification using integrated multi-omics data holds the potential to revolutionize healthcare by providing personalized treatment strategies that consider the unique molecular characteristics of each patient.
[274] Multi-Omics and its Application in Personalised Medicine Studies — Biostatistics facilitates the design and analysis of clinical trials that use multi-omics data to stratify patients and evaluate treatment effects. Multi-omic sequencing is the simultaneous analysis of multiple molecular layers (genomics, transcriptomics, proteomics, metabolomics) from a single sample to gain a comprehensive understanding of biological systems, and it plays a significant role in integrating complex data to understand biological networks and pathways. The Framingham Heart Study, which has been ongoing since 1948, has incorporated multi-omics approaches, including genomics, proteomics, and metabolomics data, to understand the complex genetic and molecular mechanisms underlying cardiovascular diseases. As technologies advance and collaborative efforts intensify, the integration of multi-omics data will continue to drive innovations in disease prevention, diagnosis, and treatment, ultimately leading to more personalised and effective healthcare solutions.
[275] Integrative Multi-Omics Approaches in Cancer Research: From Biological ... — For instance, cancer subtypes can be classified based on multi-omics datasets, such as gene expression and mutation profiles, from the same patients (Chauvel et al., 2020). A multi-omics analysis of 200 adult patients with AML showed distinct gene expression and methylation patterns across samples (Cancer Genome Atlas Research Network et al., 2013b). A multi-omics approach has also been applied to pancreatic ductal adenocarcinoma (PDAC) by integrating omics profiling of 150 patients for mutations, gene expression (mRNA, miRNA, and long non-coding RNA [lncRNA]), DNA methylation, and protein expression (Cancer Genome Atlas Research Network, 2017). Multi-omics approaches may hold the potential to study different cancer types with a high level of similarity, in terms of molecular characteristics, to basal-like breast cancer, high-grade serous ovarian cancer, and serous endometrial cancer (Cancer Genome Atlas Research Network et al., 2013a).
[276] Multiomics tools for improved atherosclerotic cardiovascular disease ... — Multiomics tools for improved atherosclerotic cardiovascular disease management - ScienceDirect Multiomics approaches are pivotal in understanding atherosclerotic cardiovascular disease (ASCVD) and offer promising preventive and therapeutic strategies beyond traditional risk factors. Artificial intelligence (AI) and machine learning (ML) models provide advanced tools for accurate ASCVD risk prediction by integrating multiomics and clinical data. Multiomics studies offer accurate preventive and therapeutic strategies for atherosclerotic cardiovascular disease (ASCVD) beyond traditional risk factors. By using artificial intelligence (AI) and machine learning (ML) approaches, it is possible to integrate multiple ‘omics and clinical data sets into tools that can be utilized for the development of personalized diagnostic and therapeutic approaches. Next article in issue For all open access content, the relevant licensing terms apply.