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Cited by 311Bioinformatics and Genomic NetworksHealth, Environment, Cognitive AgingArtificial Intelligence in Healthcare and Education

Navigating Challenges and Opportunities in Multi-Omics Integration for Personalized Healthcare

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Mohr, Alex E., Ortega-Santos, Carmen P., Whisner, Corrie M., Klein-Seetharaman, Judith, Jasbi, Paniz·2024·Biomedicines
DOI 10.3390/biomedicines12071496PMC PMC11274472
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Abstract

The field of multi-omics has witnessed unprecedented growth, converging multiple scientific disciplines and technological advances. This surge is evidenced by a more than doubling in multi-omics scientific publications within just two years (2022–2023) since its first referenced mention in 2002, as indexed by the National Library of Medicine. This emerging field has demonstrated its capability to provide comprehensive insights into complex biological systems, representing a transformative force in health diagnostics and therapeutic strategies. However, several challenges are evident when merging varied omics data sets and methodologies, interpreting vast data dimensions, streamlining longitudinal sampling and analysis, and addressing the ethical implications of managing sensitive health information. This review evaluates these challenges while spotlighting pivotal milestones: the development of targeted sampling methods, the use of artificial intelligence in formulating health indices, the integration of sophisticated n-of-1 statistical models such as digital twins, and the incorporation of blockchain technology for heightened data security. For multi-omics to truly revolutionize healthcare, it demands rigorous validation, tangible real-world applications, and smooth integration into existing healthcare infrastructures. It is imperative to address ethical dilemmas, paving the way for the realization of a future steered by omics-informed personalized medicine.

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  • Integrating heterogeneous data across multiple omics layers remains a foundational challenge in AI-driven multi-omics research for Alzheimer's disease.

    “Integrating heterogeneous data across multiple omics layers, such as genomics, transcriptomics, proteomics, metabolomics, and epigenomics, remains a foundational challenge in AI-driven multi-omics research for AD404,405.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [405]

    The Introduction (page unavailable) says that integrating and normalizing data across varied omics platforms remains difficult. Section 3 (page unavailable) discusses challenges in applying machine learning to multi-omics data. The paper does not establish this as a challenge specifically in AI-driven Alzheimer’s disease research; the search for “Alzheimer” found it only in a reference about circadian disturbances. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • The translation of AI-based multi-omics research into clinical practice for Alzheimer's disease presents a range of significant challenges spanning methodological, interpretative, infrastructural, and regulatory domains.

    “The translation of AI-based multi-omics research into clinical practice for AD presents a range of significant challenges spanning methodological, interpretative, infrastructural, and regulatory domains405,422.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [405]

    The Introduction and Section 3 describe methodological, interpretative, ethical and regulatory challenges in bringing multi-omics and AI into clinical care; the Introduction also calls for integration into existing healthcare systems. These discussions concern healthcare broadly, not translation for Alzheimer’s disease specifically. No page numbers are available in our copy. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Mohr, A. E., Ortega-Santos, C. P., Whisner, C. M., Klein-Seetharaman, J., & Jasbi, P.
Affiliations
  • Arizona State University (US)
  • Systems Precision Engineering and Advanced Research (SPEAR), Theriome Inc., Phoenix, AZ 85004, USA
Corresponding author
Jasbi, Paniz
ORCID
Venue
Biomedicines
Publisher
MDPI AG
Published
2024-07-05
Volume
12
Issue
7
Pages
1496
ISSN
2227-9059 (ISSN-L)
DOI
10.3390/biomedicines12071496
PMC
PMC11274472
OpenAlex
W4400349830
Cited by
311
References
25
Citations by year
2026: 148 · 2025: 149 · 2024: 14
FWCI
12.72
Type
journal article
Language
en
Open access
Licence
Locations
  • Biomedicines (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health (T32DK137525)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
multi-omics integration, personalized medicine, health indicators, N-of-1 studies, digital twin, blockchain data security, health data ethics, longitudinal sampling, artificial intelligence in healthcare, health diagnostics

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