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image of Integrative Multi-Omics Approaches for Personalized Medicine and Health

Abstract

Introduction

Multi-omics data integration has transformed personalized medicine, providing a comprehensive understanding of disease mechanisms and informed precision therapeutic options. Multi-omics data generated for the same samples/patients can help in getting insights into the flow of biological information at several levels, thereby providing in-depth information regarding the molecular mechanisms underlying pathological conditions. Multi-omics integration plays a pivotal role in personalized medicine by providing comprehensive insights into the complex biological systems of individual patients. This review provides a comprehensive account of the current and future progress brought into multi-omics methodologies, promising to refine diagnostics and therapeutic strategy by integrating genomic, transcriptomic analyses, proteomics approaches and metabolome screens.

Methods

A literature search was performed in PubMed using keywords like genomics, proteomics, transcriptomics, metabolomics, multi-omics, and precision medicine to identify published research articles. A thorough review of all results was then conducted, and their results and conclusions were compiled and summarized.

Result

By analyzing various omics layers, such as genomics, transcriptomics, proteomics, and metabolomics, multi-omics approaches enable the identification of patient-specific molecular traits and the discovery of new clinical therapeutics for diseases. Integration of various data types augments diagnostics, optimizes therapeutic regimens and supports personalized medicine according to an individual patient profile.

Conclusion

Integration of multi-omics data and its applications in various fields, such as cancer research, helps in optimizing patient-specific treatment and improvement of patient health. With time, as these technologies reach more people, they stand to democratize precision medicine and hopefully bridge health disparities. In conclusion, the present review highlights multiomics data integration as a transformative step towards personalized medicine and ultimately changing patient care from empirical-based to precision or individualized.

© 2025 Bentham Science Publishers
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2025-02-10
2025-06-23

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References

  1. Mohr A.E. Santos D.C.P. Whisner C.M. Seetharaman K.J. Jasbi P. Navigating challenges and opportunities in multi-omics integration for personalized healthcare. Biomedicines 2024 12 7 1496 10.3390/biomedicines12071496 39062068
    [Google Scholar]
  2. González E.A.M. Multi-omics profiles are applicable to human diseases and drug development. Biotechnology and Drug Development for Targeting Human Diseases Sharjah, U.A.E Bentham Science Publishers 2024 1 19 10.2174/9789815223163124090003
    [Google Scholar]
  3. Li J. Tian J. Liu Y. Liu Z. Tong M. Personalized analysis of human cancer multi-omics for precision oncology. Comput. Struct. Biotechnol. J. 2024 23 2049 2056 10.1016/j.csbj.2024.05.011 38783900
    [Google Scholar]
  4. Vitorino R. Navigating the omics landscape in precision medicine: a bidirectional approach to patient care. SMolecu. Cell. Proteo. 2024 1 9 10.2139/ssrn.4807692
    [Google Scholar]
  5. Cominetti O. Agarwal S. Moreno O.S. Editorial: Advances in methods and tools for multi-omics data analysis. Front. Mol. Biosci. 2023 10 1186822 10.3389/fmolb.2023.1186822 37168260
    [Google Scholar]
  6. Ahmed Z. Precision medicine with multi-omics strategies, deep phenotyping, and predictive analysis. Prog. Mol. Biol. Trans. Sci. 2022 190 1 101 125 10.1016/bs.pmbts.2022.02.002
    [Google Scholar]
  7. Yadav P. Oyeyeymi B.F. Jamling T.C. Kumar A. Bhavesh N.S. Multiomics approach for precision wellness. Epigenetics and Metabolomics. Elsevier 2021 147 180 10.1016/B978‑0‑323‑85652‑2.00004‑X
    [Google Scholar]
  8. Chakraborty S. Sharma G. Karmakar S. Banerjee S. Multi-OMICS approaches in cancer biology: New era in cancer therapy. Biochim. Biophys. Acta Mol. Basis Dis. 2024 1870 5 167120 10.1016/j.bbadis.2024.167120 38484941
    [Google Scholar]
  9. Li Y. Dong T. Wan S. Xiong R. Jin S. Dai Y. Guan C. Application of multi-omics techniques to androgenetic alopecia: Current status and perspectives. Comput. Struct. Biotechnol. J. 2024 23 2623 2636 10.1016/j.csbj.2024.06.026 39021583
    [Google Scholar]
  10. Chen C. Wang J. Pan D. Wang X. Xu Y. Yan J. Wang L. Yang X. Yang M. Liu G.P. Applications of multi‐omics analysis in human diseases. MedComm 2023 4 4 e315 10.1002/mco2.315 37533767
    [Google Scholar]
  11. Dessì A. Pintus R. Fanos V. Bosco A. Integrative multiomics approach to skin: The sinergy between individualised medicine and futuristic precision skin care? Metabolites 2024 14 3 157 10.3390/metabo14030157 38535317
    [Google Scholar]
  12. Askari S. Chapter 10 - Personalized Medicine and Genomic Research : The Future of Healthcare. Biomedical Research Developments for Improved Healthcare Pennsylvania, USA IGI Global 2024 211 234 10.4018/979‑8‑3693‑4439‑2.ch010
    [Google Scholar]
  13. P P.G.V. Shivam K. Mohan M. Prasad J. A review: Pharmacogenomics and personalized medicine. Int. J. Res. Appl. Sci. Eng. Technol. 2023 11 12 483 491 10.22214/ijraset.2023.57183
    [Google Scholar]
  14. Shivani S. Genomics in precision medicine. Int. J. Health Sci. 2022 May 5791 5798 10.53730/ijhs.v6nS3.7234
    [Google Scholar]
  15. Mubarak G. Zahir F.R. Recent major transcriptomics and epitranscriptomics contributions toward personalized and precision medicine. J. Pers. Med. 2022 12 2 199 10.3390/jpm12020199 35207687
    [Google Scholar]
  16. Iacobas D.A. Powerful quantifiers for cancer transcriptomics. World J. Clin. Oncol. 2020 11 9 679 704 10.5306/wjco.v11.i9.679 33033692
    [Google Scholar]
  17. Kim S.H. Lee S.O. Developing consilience medicine: Positron emission tomography scan and transcriptomics in pancreatic cancer. Gut Liver 2019 13 3 225 226 10.5009/gnl19116 31092726
    [Google Scholar]
  18. Wrighton K.H. Personalized DNA methylomics. Nat. Rev. Genet. 2019 20 1 4 5 10.1038/s41576‑018‑0076‑0 30443004
    [Google Scholar]
  19. Phelix C.F. Dugan J.L. Integrating information on genomics, transcriptomics, proteomics, and metabolomics into biosimulations for individualized personalized medicine 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Las Vegas, NV, USA, 24-27 February 2016, pp. 312-315. 10.1109/BHI.2016.7455897
    [Google Scholar]
  20. Schissler A.G. Gardeux V. Li Q. Achour I. Li H. Piegorsch W.W. Lussier Y.A. Dynamic changes of RNA-sequencing expression for precision medicine: N-of-1-pathways Mahalanobis distance within pathways of single subjects predicts breast cancer survival. Bioinformatics 2015 31 12 i293 i302 10.1093/bioinformatics/btv253 26072495
    [Google Scholar]
  21. Perez-Rathke A. Li H. Lussier Y.A. Interpreting personal transcriptomes: Personalized mechanism-scale profiling of RNA-SEQ data. Biocomputing. 2012 2013 159 170 10.1142/9789814447973_0016
    [Google Scholar]
  22. Verweij C. L. Transcript profiling towards personalised medicine in rheumatoid arthritis. Neth. J. Med. 2009 67 11 364 371
    [Google Scholar]
  23. Su J. Yang L. Sun Z. Zhan X. Personalized drug therapy: Innovative concept guided with proteoformics. Mol. Cell. Proteomics 2024 23 3 100737 10.1016/j.mcpro.2024.100737 38354979
    [Google Scholar]
  24. Bastaki K. Umlai U-K.I. Jithesh P.V. Personalized medicine. Metabolomics. New York Elsevier 2023 1 32 10.1016/B978‑0‑323‑99924‑3.00004‑2
    [Google Scholar]
  25. Santamaría E. Towards precision prognostication and personalized therapeutics through proteomics. Int. J. Mol. Sci. 2023 24 7 6361 10.3390/ijms24076361 37047334
    [Google Scholar]
  26. Liotta L.A. Pappalardo P.A. Carpino A. Haymond A. Howard M. Espina V. Wulfkuhle J. Petricoin E. Laser Capture Proteomics: Spatial tissue molecular profiling from the bench to personalized medicine. Expert Rev. Proteomics 2021 18 10 845 861 10.1080/14789450.2021.1984886 34607525
    [Google Scholar]
  27. Cao X. Xing J. PrecisionProDB: Improving the proteomics performance for precision medicine. Bioinformatics 2021 37 19 3361 3363 10.1093/bioinformatics/btab218 33787868
    [Google Scholar]
  28. Vasdev K. Proteomics: Applications in disease diagnosis to develop precision medicine. Bioinf. & Prot. Open Access J. 2020 4 1 10.23880/bpoj‑16000129
    [Google Scholar]
  29. Simpson C.E. Ledford J.G. Liu G. Application of metabolomics across the spectrum of pulmonary and critical care medicine. Am. J. Respir. Cell Mol. Biol. 2024 71 1 1 9 10.1165/rcmb.2024‑0080PS 38547373
    [Google Scholar]
  30. Sri Sai Meghana I. Bhat R.A. Bhandary R. Metabolomics: A step towards personalized periodontal diagnosis. Res. J. Phar. Tech. 2023 Nov 5439 5443 10.52711/0974‑360X.2023.00881
    [Google Scholar]
  31. Hotea I. Sirbu C. Plotuna A.M. Tîrziu E. Badea C. Berbecea A. Dragomirescu M. Radulov I. Integrating (nutri-)metabolomics into the one health tendency—the key for personalized medicine advancement. Metabolites 2023 13 7 800 10.3390/metabo13070800 37512507
    [Google Scholar]
  32. Rahman M. Industrial application of metabolomics for personalized medicine: Current status and challenges. Metabolomics. New York Elsevier 2023 261 291 10.1016/B978‑0‑323‑99924‑3.00009‑1
    [Google Scholar]
  33. Zhou J. Zhong L. Applications of liquid chromatography-mass spectrometry based metabolomics in predictive and personalized medicine. Front. Mol. Biosci. 2022 9 1049016 10.3389/fmolb.2022.1049016 36406271
    [Google Scholar]
  34. Chowdhury S. Faheem S.M. Nawaz S.S. Siddiqui K. The role of metabolomics in personalized medicine for diabetes. Per. Med. 2021 18 5 501 508 10.2217/pme‑2021‑0083 34406076
    [Google Scholar]
  35. Gupta M.K. Peng H. Li Y. Xu C.J. The role of DNA methylation in personalized medicine for immune-related diseases. Pharmacol. Ther. 2023 250 108508 10.1016/j.pharmthera.2023.108508 37567513
    [Google Scholar]
  36. Rocha G. Gomes J. Leite M. Cunha d.N. B. Costa F. Epigenome-driven strategies for personalized cancer immunotherapy. Cancer Manag. Res. 2023 15 1351 1367 10.2147/CMAR.S272031
    [Google Scholar]
  37. Auroy L. Louvel S. Épigénétique et cancérologie. médecine/sciences 2022 38 3 296 302 10.1051/medsci/2022025
    [Google Scholar]
  38. Riso D.G. Cocozza S. Artificial intelligence for epigenetics: Towards personalized medicine. Curr. Med. Chem. 2021 28 32 6654 6674 10.2174/0929867327666201117142006 33208060
    [Google Scholar]
  39. Sabir S.B. A review on epigenetics of human inherited diseases: Molecular diagnosis. Inter. J. Inno. Sci. Res. Tech. 2024 Jul 696 703 10.38124/ijisrt/IJISRT24JUL447
    [Google Scholar]
  40. Sgro A. Blancafort P. Epigenome engineering: New technologies for precision medicine. Nucleic Acids Res. 2020 48 22 12453 12482 10.1093/nar/gkaa1000 33196851
    [Google Scholar]
  41. Majchrzak-Celińska A. Dubowska B.W. Pharmacoepigenetics: Basic Principles for Personalized Medicine. Pharmacoepigenetics. New York Elsevier 2019 101 112 10.1016/B978‑0‑12‑813939‑4.00002‑4
    [Google Scholar]
  42. Meral G. Aslan E.S. Tunaligil V. Burkay N. Acar A.E.G. Alp M.Y. The importance of nutrigenetics and microbiota in personalized medicine: From phenotype to genotype. Cureus 2024 16 5 e61256 10.7759/cureus.61256 38807972
    [Google Scholar]
  43. Zhao Q. Chen Y. Huang W. Zhou H. Zhang W. Drug-microbiota interactions: An emerging priority for precision medicine. Signal Transduct. Target. Ther. 2023 8 1 386 10.1038/s41392‑023‑01619‑w 37806986
    [Google Scholar]
  44. Wu J. Singleton S.S. Bhuiyan U. Krammer L. Mazumder R. Multi-omics approaches to studying gastrointestinal microbiome in the context of precision medicine and machine learning. Front. Mol. Biosci. 2024 10 1337373 10.3389/fmolb.2023.1337373 38313584
    [Google Scholar]
  45. Zehrh I. Metagenomics and machine learning-based precision medicine approaches for autoimmune diseases. Mach. learn. 2023 2 2023040209 10.20944/preprints202304.0209.v2
    [Google Scholar]
  46. Sadee W. Personalized therapeutics and pharmacogenomics: Integral to personalized health care. Pharm. Res. 2017 34 8 1535 1538 10.1007/s11095‑017‑2170‑y 28493098
    [Google Scholar]
  47. Sychev D.A. “Multiomic” studies as a promising clinical pharmacological tool for personalization of socially significant diseases pharmacotherapy in russia. Personal. Psychiat. Neurol. 2022 2 2 1 2 10.52667/2712‑9179‑2022‑2‑2‑1‑2
    [Google Scholar]
  48. Ahmed Z. Multi-omics strategies for personalized and predictive medicine: Past, current, and future translational opportunities. Emerg. Top. Life Sci. 2022 6 2 215 225 10.1042/ETLS20210244 35234253
    [Google Scholar]
  49. Toward multi-ethnic and multiomic: Capturing the complete clinical picture to go beyond european-centric precision medicine. Inside Prec. Med. 2022 9 S1 18 18 10.1089/ipm.09.S1.04
    [Google Scholar]
  50. Guo Y. Luo L. Zhu J. Li C. Multi-omics research strategies for psoriasis and atopic dermatitis. Int. J. Mol. Sci. 2023 24 9 8018 10.3390/ijms24098018 37175722
    [Google Scholar]
  51. Ugidos M. Tarazona S. Montalbán P.J.M. Ferrer A. Conesa A. MultiBaC: A strategy to remove batch effects between different omic data types. Stat. Methods Med. Res. 2020 29 10 2851 2864 10.1177/0962280220907365 32131696
    [Google Scholar]
  52. Jiang M.Z. Aguet F. Ardlie K. Chen J. Cornell E. Cruz D. Durda P. Gabriel S.B. Gerszten R.E. Guo X. Johnson C.W. Kasela S. Lange L.A. Lappalainen T. Liu Y. Reiner A.P. Smith J. Sofer T. Taylor K.D. Tracy R.P. VanDenBerg D.J. Wilson J.G. Rich S.S. Rotter J.I. Love M.I. Raffield L.M. Li Y. Canonical correlation analysis for multi-omics: Application to cross-cohort analysis. PLoS Genet. 2023 19 5 e1010517 10.1371/journal.pgen.1010517 37216410
    [Google Scholar]
  53. Jeon J. Han E.Y. Jung I. MOPA: An integrative multi-omics pathway analysis method for measuring omics activity. PLoS One 2023 18 3 e0278272 10.1371/journal.pone.0278272 36928437
    [Google Scholar]
  54. Qin G. Liu Z. Xie L. Multiple Omics Data Integration. Systems Medicine. New York Elsevier 2021 103 115 10.1016/B978‑0‑12‑801238‑3.11508‑9
    [Google Scholar]
  55. Gutierrez D. B. Supporting information an integrated, high-throughput strategy for multi-omic systems level analysis. J. Proteome. Res. 2018 17 10 3396 3408
    [Google Scholar]
  56. Marques F.B. Leal G.F. Bettoni G.N. Souza d.O.N. Integration of Bioinformatics and Clinical Data to Personalized Precision Medicine. ITNG 2021 18th International Conference on Information Technology-New Generations Cham Springer International Publishing 2021 179 184 10.1007/978‑3‑030‑70416‑2_23
    [Google Scholar]
  57. Guha P. Dutta S. Murti K. Charan J.K. Pandey K. Ravichandiran V. Dhingra S. The integration of omics: A promising approach to personalized tuberculosis treatment. Medicine in Omics 2024 12 100033 10.1016/j.meomic.2024.100033
    [Google Scholar]
  58. Zhang X. Kuivenhoven J.A. Groen A.K. Forward individualized medicine from personal genomes to interactomes. Front. Physiol. 2015 6 364 10.3389/fphys.2015.00364 26696898
    [Google Scholar]
  59. Ahmed Z. Practicing precision medicine with intelligently integrative clinical and multi-omics data analysis. Hum. Genomics 2020 14 1 35 10.1186/s40246‑020‑00287‑z 33008459
    [Google Scholar]
  60. Shi J. Zhao J. Zhang Y. Wang Y. Tan C.P. Xu Y.J. Liu Y. Windows scanning multiomics: Integrated metabolomics and proteomics. Anal. Chem. 2023 95 51 18793 18802 10.1021/acs.analchem.3c03785 38095040
    [Google Scholar]
  61. Ponzi E. Thoresen M. Nøst H.T. Møllersen K. Integrative, multi-omics, analysis of blood samples improves model predictions: Applications to cancer. BMC Bioinformatics 2021 22 1 395 10.1186/s12859‑021‑04296‑0 34353282
    [Google Scholar]
  62. Xu A. Chen J. Peng H. Han G. Cai H. Simultaneous interrogation of cancer omics to identify subtypes with significant clinical differences. Front. Genet. 2019 10 236 10.3389/fgene.2019.00236 30984238
    [Google Scholar]
  63. Chen T. Abadi A.J. Cao L.K.A. Tyagi S. multiomics: A user-friendly multi-omics data harmonisation R pipeline. F1000 Res. 2021 10 538 10.12688/f1000research.53453.1
    [Google Scholar]
  64. Filippo D.M. Pescini D. Galuzzi B.G. Bonanomi M. Gaglio D. Mangano E. Consolandi C. Alberghina L. Vanoni M. Damiani C. INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation. PLOS Comput. Biol. 2022 18 2 e1009337 10.1371/journal.pcbi.1009337 35130273
    [Google Scholar]
  65. Filippo D.M. INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation. PLoS. Comput. Bio. 2021 18 2 e1009337 10.1101/2021.08.13.456220
    [Google Scholar]
  66. Ciaramella A. Nardone D. Staiano A. Data integration by fuzzy similarity-based hierarchical clustering. BMC Bioinformatics 2020 21 S10 Suppl. 10 350 10.1186/s12859‑020‑03567‑6 32838739
    [Google Scholar]
  67. Mao H. HALO: Hierarchical Causal Modeling for Single Cell Multi-Omics Data. bioRxiv 2022 1 6 10.1101/2022.10.17.512602
    [Google Scholar]
  68. Lee B. Zhang S. Poleksic A. Xie L. Heterogeneous multi-layered network model for omics data integration and analysis. Front. Genet. 2020 10 1381 10.3389/fgene.2019.01381 32063919
    [Google Scholar]
  69. Denis M. Tadesse M.G. Evaluation of hierarchical models for integrative genomic analyses. Bioinformatics 2016 32 5 738 746 10.1093/bioinformatics/btv653 26545823
    [Google Scholar]
  70. Vetrano S. Bouma G. Benschop R.J. Birngruber T. Costanzo A. D’Haens G.R.A.M. Frasca L. Hillenbrand R. Iversen L. Johansen C. Kaser A. Koenen H.J.P.M. Noehammer C. Biroulet P.L. Raes J. Ricotti L. Rosenstiel P. Satagopam V.P. Schreiber S. Vermeire S. Wollenberg A. Weidinger S. Ziemek D. Danese S. ImmUniverse Consortium: Multi-omics integrative approach in personalized medicine for immune-mediated inflammatory diseases. Front. Immunol. 2022 13 1002629 10.3389/fimmu.2022.1002629 36439150
    [Google Scholar]
  71. Li C.X. Chen H. Kermani Z.N. Adcock I.M. Sköld C.M. Zhou M. Wheelock Å.M. Consensus clustering with missing labels (ccml): A consensus clustering tool for multi-omics integrative prediction in cohorts with unequal sample coverage. Brief. Bioinform. 2023 25 1 bbad501 10.1093/bib/bbad501 38205966
    [Google Scholar]
  72. Brière G. Darbo É. Thébault P. Uricaru R. Consensus clustering applied to multi-omics disease subtyping. BMC Bioinformatics 2021 22 1 361 10.1186/s12859‑021‑04279‑1 34229612
    [Google Scholar]
  73. Wang R.S. Maron B.A. Loscalzo J. Multiomics network medicine approaches to precision medicine and therapeutics in cardiovascular diseases. Arterioscler. Thromb. Vasc. Biol. 2023 43 4 493 503 10.1161/ATVBAHA.122.318731 36794589
    [Google Scholar]
  74. Güngör B.B. Network And Pathway Based Analysis Of Multi-Omic Data To Enlighten Molecular Mechanisms Of Complex Diseases. Sağlık Bilimlerinde İleri Araştırmalar Dergisi Istanbul Istanbul University 2022 5 S-1 65 65 10.26650/JARHS2021‑1137390
    [Google Scholar]
  75. Wang C. Lue W. Kaalia R. Kumar P. Rajapakse J.C. Network-based integration of multi-omics data for clinical outcome prediction in neuroblastoma. Sci. Rep. 2022 12 1 15425 10.1038/s41598‑022‑19019‑5 36104347
    [Google Scholar]
  76. Comte B. Baumbach J. Benis A. Basílio J. Debeljak N. Flobak Å. Franken C. Harel N. He F. Kuiper M. Pérez M.J.A. Guillot P.E. Režen T. Rozman D. Schmid J.A. Scerri J. Tieri P. Steen v.K. Vasudevan S. Watterson S. Schmidt H.H.H.W. Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine. Net. Sys. Med. 2020 3 1 67 90 10.1089/nsm.2020.0004 32954378
    [Google Scholar]
  77. Ranjbari S. Arslanturk S. Integration of incomplete multi-omics data using Knowledge Distillation and Supervised Variational Autoencoders for disease progression prediction. J. Biomed. Inform. 2023 147 104512 10.1016/j.jbi.2023.104512 37813325
    [Google Scholar]
  78. Acharya D. Mukhopadhyay A. A comprehensive review of machine learning techniques for multi-omics data integration: Challenges and applications in precision oncology. Brief. Funct. Genomics 2024 23 5 549 560 10.1093/bfgp/elae013 38600757
    [Google Scholar]
  79. Aguila C.R. Pupo A.N. Rodríguez H.E.W. Multi-omics data integration approaches for precision oncology. Mol. Omics 2022 18 6 469 479 10.1039/D1MO00411E 35470819
    [Google Scholar]
  80. Athieniti E. Spyrou G.M. A guide to multi-omics data collection and integration for translational medicine. Comput. Struct. Biotechnol. J. 2023 21 134 149 10.1016/j.csbj.2022.11.050 36544480
    [Google Scholar]
  81. Heo Y.J. Hwa C. Lee G.H. Park J.M. An J.Y. Integrative multi-omics approaches in cancer research: From biological networks to clinical subtypes. Mol. Cells 2021 44 7 433 443 10.14348/molcells.2021.0042 34238766
    [Google Scholar]
  82. Karaman D.E. Işık Z. Multi-omics data analysis identifies prognostic biomarkers across cancers. Med. Sci. 2023 11 3 44 10.3390/medsci11030044 37489460
    [Google Scholar]
  83. Vahabi N. Michailidis G. Unsupervised multi-omics data integration methods: A comprehensive review. Front. Genet. 2022 13 854752 10.3389/fgene.2022.854752 35391796
    [Google Scholar]
  84. Demirel H.C. Arici M.K. Tuncbag N. Computational approaches leveraging integrated connections of multi-omic data toward clinical applications. Mol. Omics 2022 18 1 7 18 10.1039/D1MO00158B 34734935
    [Google Scholar]
  85. DeGroat W. Abdelhalim H. Patel K. Mendhe D. Zeeshan S. Ahmed Z. Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine. Sci. Rep. 2024 14 1 1 10.1038/s41598‑023‑50600‑8 38167627
    [Google Scholar]
  86. Wu Y. Xie L. AI-driven multi-omics integration for multi-scale predictive modeling of causal genotype-environment-phenotype relationships. arXiv:2407.06405 2024 1 6
    [Google Scholar]
  87. Benkirane H. Pradat Y. Michiels S. Cournède P.H. CustOmics: A versatile deep-learning based strategy for multi-omics integration. PLOS Comput. Biol. 2023 19 3 e1010921 10.1371/journal.pcbi.1010921 36877736
    [Google Scholar]
  88. Itai Y. Rappoport N. Shamir R. Integration of gene expression and DNA methylation data across different experiments. Nucleic Acids Res. 2023 51 15 7762 7776 10.1093/nar/gkad566 37395437
    [Google Scholar]
  89. Ji Y. Dutta P. Davuluri R. Deep multi-omics integration by learning correlation-maximizing representation identifies prognostically stratified cancer subtypes. Bioinformat. Adv. 2023 3 1 vbad075 10.1093/bioadv/vbad075 37424943
    [Google Scholar]
  90. Chierici M. Bussola N. Marcolini A. Francescatto M. Zandonà A. Trastulla L. Agostinelli C. Jurman G. Furlanello C. Integrative network fusion: A multi-omics approach in molecular profiling. Front. Oncol. 2020 10 1065 10.3389/fonc.2020.01065 32714870
    [Google Scholar]
  91. Garmire L.X. Strategies to integrate multi-omics data for patient survival prediction. arXiv preprint arXiv:2008.12455 2020 1 6
    [Google Scholar]
  92. Shen S. Editorial: Integrative approaches to analyze cancer based on multi‐omics. Front. Genet. 2022 13 1057408 10.3389/fgene.2022.1057408 36324507
    [Google Scholar]
  93. Wess M. Spatial integration of multi-omics data using the novel multi-omics imaging integration toolset. bioRxiv 2024 1 6 10.1101/2024.06.11.598306
    [Google Scholar]
  94. Zhou L. Zhu Z. Wang X. Gao H. Multi-Omics Analysis for Improved Diagnosis, Prognosis, and Drug Response Prediction in Digestive System Tumors. 2023 IEEE International Conference on Unmanned Systems (ICUS) Hefei, China, 13-15 October 2023, pp. 505-511. 10.1109/ICUS58632.2023.10318469
    [Google Scholar]
  95. Koen N. Preez D.I. Loots D.T. Metabolomics and Personalized Medicine. Front. Genet. 2016 53 78 10.1016/bs.apcsb.2015.09.003
    [Google Scholar]
  96. Zhang X. Wang P. Qin J. Editorial: Enhanced biological mechanism study, drug discovery and individualized medicine with single-cell multiomics data and integrative analysis. Front. Genet. 2023 14 1236126 10.3389/fgene.2023.1236126 37434951
    [Google Scholar]
  97. Ow G.S. Tang Z. Kuznetsov V.A. Big data and computational biology strategy for personalized prognosis. Oncotarget 2016 7 26 40200 40220 10.18632/oncotarget.9571 27229533
    [Google Scholar]
  98. Ivanisevic T. Sewduth R.N. Multi-omics integration for the design of novel therapies and the identification of novel biomarkers. Proteomes 2023 11 4 34 10.3390/proteomes11040034 37873876
    [Google Scholar]
  99. Tang L. Zeng X. “#2347 Multi-omics analysis identifies traditional Chinese medicine targets for chronic kidney disease,”. Nephrology Dialysis Transplantation. 2024 39 Supplement_1 gfae069 0717 10.1093/ndt/gfae069.717
    [Google Scholar]
  100. Wang L. Multi-omics Identify Serotonin Transporter as a Promising Therapeutic Target for Essential Tremor. bioRxiv 2024 1 6 10.1101/2024.03.18.585649
    [Google Scholar]
  101. Moni M.A. Liò P. How to build personalized multi-omics comorbidity profiles. Front. Cell Dev. Biol. 2015 3 28 10.3389/fcell.2015.00028 26157799
    [Google Scholar]
  102. Hameed H. Multiomics approaches in cancer. Biological Insights of Multi-Omics Technologies in Human Diseases. Elsevier 2024 53 72 10.1016/B978‑0‑443‑23971‑7.00003‑1
    [Google Scholar]
  103. Martins Y. Multi-task analysis of gene expression data on cancer public datasets. medRxiv 2023 1 6 10.1101/2023.09.27.23296213
    [Google Scholar]
  104. Bansal B. Sahoo A. Multi-omics data fusion using adaptive GTO guided Non-negative matrix factorization for cancer subtype discovery. Comput. Methods Programs Biomed. 2023 228 107246 10.1016/j.cmpb.2022.107246 36434961
    [Google Scholar]
  105. Çeker D.D. Baysungur V. Evman S. Kolbaş İ. Gördebi̇l A. Nalbantoğlu S.M. Tambağ Y. Kaçar Ö. Mi̇di̇ A. Aslanoğlu H. Kara N. Algan N. Boyacioğlu A. Yilmaz K.B. Şahi̇n A. Polat U.H. Şehi̇toğullari A. Çibikdi̇ken A.O. Büyükyilmaz M. Aydi̇lek İ.B. Eneş A. Küçüker S. Karakaya F. Boyaci İ. Gümüş M. Şenol O. Öztuğ M. Saban E. Soysal Ö. Büyükpinarbaşili N. Turna A. Günlüoğlu M.Z. Çakir A. Teki̇n Ş. Tazebay U. Karadağ A. LUNGBANK: A novel biorepository strategy tailored for comprehensive multiomics analysis and P-medicine applications in lung cancer. Turk. J. Biol. 2024 48 3 203 217 10.55730/1300‑0152.2696 39050710
    [Google Scholar]
  106. Wang C. Lye X. Kaalia R. Kumar P. Rajapakse J.C. Deep learning and multi-omics approach to predict drug responses in cancer. BMC Bioinformatics 2022 22 S10 Suppl. 10 632 10.1186/s12859‑022‑04964‑9 36443676
    [Google Scholar]
  107. Sethi Y. Patel N. Kaka N. Kaiwan O. Kar J. Moinuddin A. Goel A. Chopra H. Cavalu S. Precision medicine and the future of cardiovascular diseases: A clinically oriented comprehensive review. J. Clin. Med. 2023 12 5 1799 10.3390/jcm12051799 36902588
    [Google Scholar]
  108. Chouvarda I. Maglaveras N. Title Cardiovascular Big Data Analytics. Cardiovascular Computing—Methodologies and Clinical Applications. Series in BioEngineering. Singapore Springer 2019 303 313 10.1007/978‑981‑10‑5092‑3_15
    [Google Scholar]
  109. Han Y. Zeng X. Hua L. Quan X. Chen Y. Zhou M. Chuang Y. Li Y. Wang S. Shen X. Wei L. Yuan Z. Zhao Y. The fusion of multi-omics profile and multimodal EEG data contributes to the personalized diagnostic strategy for neurocognitive disorders. Microbiome 2024 12 1 12 10.1186/s40168‑023‑01717‑5 38243335
    [Google Scholar]
  110. Ekvall S. Westermarck T. Havia M. Atroshi F. Personalized Management of Selected Neurological Disorders Personalized Medicine, in Relation to Redox State, Diet and Lifestyle. London IntechOpen 2020 10.5772/intechopen.92002
    [Google Scholar]
  111. Xu Z. Li W. Dong X. Chen Y. Zhang D. Wang J. Zhou L. He G. Precision medicine in colorectal cancer: Leveraging multi-omics, spatial omics, and artificial intelligence. Clin. Chim. Acta 2024 559 119686 10.1016/j.cca.2024.119686 38663471
    [Google Scholar]
  112. Moreno C. Laura M. Multi-omics quality assessment in personalized medicine through eatris. bioRxiv 2023 10 10.1101/2023.10.25.563912
    [Google Scholar]
  113. Shaba E. Multi-omics integrative approach of extracellular vesicles: A future challenging milestone. Proteomes. 2022 10 2 12 10.3390/proteomes10020012
    [Google Scholar]
  114. Zhu X. Yao Q. Yang P. Zhao D. Yang R. Bai H. Ning K. Multi-omics approaches for in-depth understanding of therapeutic mechanism for Traditional Chinese Medicine. Front. Pharmacol. 2022 13 1031051 10.3389/fphar.2022.1031051 36506559
    [Google Scholar]
/content/journals/cbio/10.2174/0115748936360644250127095005
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Integrative Multi-Omics Approaches for Personalized Medicine and Health
Bentham Science Publishers ; https://doi.org/10.2174/0115748936360644250127095005
/content/journals/cbio/10.2174/0115748936360644250127095005
/content/journals/cbio/10.2174/0115748936360644250127095005
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  • Article Type:     Review Article
Keywords: personalized medicine ; transcriptomics ; proteomics ; genomics ; Multi-omics integration

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