Skip to content
2000
Volume 21, Issue 2
  • ISSN: 1574-8936
  • E-ISSN: 2212-392X

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.

Results

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.

© 2026 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cbio/10.2174/0115748936360644250127095005
2025-02-10
2026-02-10

  • From This Site

    /content/journals/cbio/10.2174/0115748936360644250127095005
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. MohrA.E. SantosD.C.P. WhisnerC.M. SeetharamanK.J. JasbiP. Navigating challenges and opportunities in multi-omics integration for personalized healthcare.Biomedicines2024127149610.3390/biomedicines12071496 39062068
     [Google Scholar]
  2. GonzálezE.A.M. Benítez-FloresJdC Gómez-VerjanJC. Multi-omics profiles are applicable to human diseases and drug development. Biotechnology and drug development for targeting human diseases.Sharjah, U.A.EBentham Science Publishers202411910.2174/9789815223163124090003
     [Google Scholar]
  3. LiJ. TianJ. LiuY. LiuZ. TongM. Personalized analysis of human cancer multi-omics for precision oncology.Comput. Struct. Biotechnol. J.2024232049205610.1016/j.csbj.2024.05.011 38783900
     [Google Scholar]
  4. VitorinoR. Navigating the omics landscape in precision medicine: A bidirectional approach to patient care.SMolecu Cell Proteo20241910.2139/ssrn.4807692
     [Google Scholar]
  5. CominettiO. AgarwalS. MorenoO.S. Editorial: Advances in methods and tools for multi-omics data analysis.Front. Mol. Biosci.202310118682210.3389/fmolb.2023.1186822 37168260
     [Google Scholar]
  6. AhmedZ. Precision medicine with multi-omics strategies, deep phenotyping, and predictive analysis.Prog. Mol. Biol. Transl. Sci.2022190110112510.1016/bs.pmbts.2022.02.002
     [Google Scholar]
  7. YadavP. OyeyeymiB.F. JamlingT.C. KumarA. BhaveshN.S. Multiomics approach for precision wellness. Epigenetics and Metabolomics.Elsevier202114718010.1016/B978‑0‑323‑85652‑2.00004‑X
     [Google Scholar]
  8. ChakrabortyS. SharmaG. KarmakarS. BanerjeeS. Multi-OMICS approaches in cancer biology: New era in cancer therapy.Biochim. Biophys. Acta Mol. Basis Dis.20241870516712010.1016/j.bbadis.2024.167120 38484941
     [Google Scholar]
  9. LiY. DongT. WanS. Application of multi-omics techniques to androgenetic alopecia: Current status and perspectives.Comput. Struct. Biotechnol. J.2024232623263610.1016/j.csbj.2024.06.026 39021583
     [Google Scholar]
  10. ChenC. WangJ. PanD. Applications of multi‐omics analysis in human diseases.MedComm202344e31510.1002/mco2.315 37533767
     [Google Scholar]
  11. DessìA. PintusR. FanosV. BoscoA. Integrative multiomics approach to skin: The sinergy between individualised medicine and futuristic precision skin care?Metabolites202414315710.3390/metabo14030157 38535317
     [Google Scholar]
  12. AskariS. Chapter 10 - Personalized Medicine and Genomic Research: The Future of Healthcare. In: Biomedical Research Developments for Improved Healthcare.Chapter 10Pennsylvania, USAIGI Global202421123410.4018/979‑8‑3693‑4439‑2.ch010
     [Google Scholar]
  13. PPGV ShivamK MohanM PrasadJ. A review: Pharmacogenomics and personalized medicine.Int. J. Res. Appl. Sci. Eng. Technol.2023111248349110.22214/ijraset.2023.57183
     [Google Scholar]
  14. ShivaniS. Genomics in precision medicine.Int. J. Health Sci.2022May5791579810.53730/ijhs.v6nS3.7234
     [Google Scholar]
  15. MubarakG. ZahirF.R. Recent major transcriptomics and epitranscriptomics contributions toward personalized and precision medicine.J. Pers. Med.202212219910.3390/jpm12020199 35207687
     [Google Scholar]
  16. IacobasD.A. Powerful quantifiers for cancer transcriptomics.World J. Clin. Oncol.202011967970410.5306/wjco.v11.i9.679 33033692
     [Google Scholar]
  17. KimS.H. LeeS.O. Developing consilience medicine: Positron emission tomography scan and transcriptomics in pancreatic cancer.Gut Liver201913322522610.5009/gnl19116 31092726
     [Google Scholar]
  18. WrightonK.H. Personalized DNA methylomics.Nat. Rev. Genet.20192014510.1038/s41576‑018‑0076‑0 30443004
     [Google Scholar]
  19. PhelixC.F. DuganJ.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. 312-31510.1109/BHI.2016.7455897
     [Google Scholar]
  20. SchisslerA.G. GardeuxV. LiQ. Dynamic changes of RNA-sequencing expression for precision medicine: N-of-1-pathways Mahalanobis distance within pathways of single subjects predicts breast cancer survival.Bioinformatics20153112i293i30210.1093/bioinformatics/btv253 26072495
     [Google Scholar]
  21. Perez-RathkeA. LiH. LussierY.A. Interpreting personal transcriptomes: Personalized mechanism-scale profiling of RNA-SEQ data.Biocomputing2012201315917010.1142/9789814447973_0016
     [Google Scholar]
  22. VerweijC.L. Transcript profiling towards personalised medicine in rheumatoid arthritis.Neth. J. Med.20096711364371
     [Google Scholar]
  23. SuJ. YangL. SunZ. ZhanX. Personalized drug therapy: Innovative concept guided with proteoformics.Mol. Cell. Proteomics202423310073710.1016/j.mcpro.2024.100737 38354979
     [Google Scholar]
  24. BastakiK. UmlaiU-K.I. JitheshP.V. Personalized medicine.In: Metabolomics. New York202313210.1016/B978‑0‑323‑99924‑3.00004‑2
     [Google Scholar]
  25. SantamaríaE. Towards precision prognostication and personalized therapeutics through proteomics.Int. J. Mol. Sci.2023247636110.3390/ijms24076361 37047334
     [Google Scholar]
  26. LiottaL.A. PappalardoP.A. CarpinoA. Laser Capture Proteomics: Spatial tissue molecular profiling from the bench to personalized medicine.Expert Rev. Proteomics2021181084586110.1080/14789450.2021.1984886 34607525
     [Google Scholar]
  27. CaoX. XingJ. PrecisionProDB: Improving the proteomics performance for precision medicine.Bioinformatics202137193361336310.1093/bioinformatics/btab218 33787868
     [Google Scholar]
  28. VasdevK. Proteomics: Applications in disease diagnosis to develop precision medicine.Bioinf & Prot Open Access J20204110.23880/bpoj‑16000129
     [Google Scholar]
  29. SimpsonC.E. LedfordJ.G. LiuG. Application of metabolomics across the spectrum of pulmonary and critical care medicine.Am. J. Respir. Cell Mol. Biol.20247111910.1165/rcmb.2024‑0080PS 38547373
     [Google Scholar]
  30. Sri Sai MeghanaI. BhatR.A. BhandaryR. Metabolomics: A step towards personalized periodontal diagnosis.Res J Phar Tech2023Nov5439544310.52711/0974‑360X.2023.00881
     [Google Scholar]
  31. HoteaI. SirbuC. PlotunaA.M. Integrating (nutri-) metabolomics into the one health tendency—The key for personalized medicine advancement.Metabolites202313780010.3390/metabo13070800 37512507
     [Google Scholar]
  32. RahmanM. Industrial application of metabolomics for personalized medicine: Current status and challenges. In: Metabolomics.New YorkElsevier202326129110.1016/B978‑0‑323‑99924‑3.00009‑1
     [Google Scholar]
  33. ZhouJ. ZhongL. Applications of liquid chromatography-mass spectrometry based metabolomics in predictive and personalized medicine.Front. Mol. Biosci.20229104901610.3389/fmolb.2022.1049016 36406271
     [Google Scholar]
  34. ChowdhuryS. FaheemS.M. NawazS.S. SiddiquiK. The role of metabolomics in personalized medicine for diabetes.Per. Med.202118550150810.2217/pme‑2021‑0083 34406076
     [Google Scholar]
  35. GuptaM.K. PengH. LiY. XuC.J. The role of DNA methylation in personalized medicine for immune-related diseases.Pharmacol. Ther.202325010850810.1016/j.pharmthera.2023.108508 37567513
     [Google Scholar]
  36. RochaG. GomesJ. LeiteM. Cunha dNB CostaF. Epigenome-driven strategies for personalized cancer immunotherapy.Cancer Manag. Res.2023151351136710.2147/CMAR.S272031
     [Google Scholar]
  37. AuroyL LouvelS. Épigénétique et cancérologie.médecine/sciences202238329630210.1051/medsci/2022025
     [Google Scholar]
  38. RisoD.G. CocozzaS. Artificial intelligence for epigenetics: Towards personalized medicine.Curr. Med. Chem.202128326654667410.2174/0929867327666201117142006 33208060
     [Google Scholar]
  39. SabirS.B. A review on epigenetics of human inherited diseases: Molecular diagnosis.Inter J Inno Sci Res Tech2024Jul69670310.38124/ijisrt/IJISRT24JUL447
     [Google Scholar]
  40. SgroA. BlancafortP. Epigenome engineering: New technologies for precision medicine.Nucleic Acids Res.20204822124531248210.1093/nar/gkaa1000 33196851
     [Google Scholar]
  41. Majchrzak-CelińskaA. DubowskaB.W. Pharmacoepigenetics: Basic principles for personalized medicine. In: Pharmacoepigenetics.New YorkElsevier201910111210.1016/B978‑0‑12‑813939‑4.00002‑4
     [Google Scholar]
  42. MeralG. AslanE.S. TunaligilV. BurkayN. AcarA.E.G. AlpM.Y. The importance of nutrigenetics and microbiota in personalized medicine: From phenotype to genotype.Cureus2024165e6125610.7759/cureus.61256 38807972
     [Google Scholar]
  43. ZhaoQ. ChenY. HuangW. ZhouH. ZhangW. Drug-microbiota interactions: An emerging priority for precision medicine.Signal Transduct. Target. Ther.20238138610.1038/s41392‑023‑01619‑w 37806986
     [Google Scholar]
  44. WuJ. SingletonS.S. BhuiyanU. KrammerL. MazumderR. Multi-omics approaches to studying gastrointestinal microbiome in the context of precision medicine and machine learning.Front. Mol. Biosci.202410133737310.3389/fmolb.2023.1337373 38313584
     [Google Scholar]
  45. ZehrhI. Metagenomics and machine learning-based precision medicine approaches for autoimmune diseases.Mach. Learn.20232202304020910.20944/preprints202304.0209.v2
     [Google Scholar]
  46. SadeeW. Personalized therapeutics and pharmacogenomics: Integral to personalized health care.Pharm. Res.20173481535153810.1007/s11095‑017‑2170‑y 28493098
     [Google Scholar]
  47. SychevD.A. “Multiomic” studies as a promising clinical pharmacological tool for personalization of socially significant diseases pharmacotherapy in russia.Personal Psychiat Neurol2022221210.52667/2712‑9179‑2022‑2‑2‑1‑2
     [Google Scholar]
  48. AhmedZ. Multi-omics strategies for personalized and predictive medicine: Past, current, and future translational opportunities.Emerg. Top. Life Sci.20226221522510.1042/ETLS20210244 35234253
     [Google Scholar]
  49. Toward multi-ethnic and multiomic: Capturing the complete clinical picture to go beyond european-centric precision medicine.Inside Prec Med20229S118810.1089/ipm.09.S1.04
     [Google Scholar]
  50. GuoY. LuoL. ZhuJ. LiC. Multi-omics research strategies for psoriasis and atopic dermatitis.Int. J. Mol. Sci.2023249801810.3390/ijms24098018 37175722
     [Google Scholar]
  51. UgidosM. TarazonaS. MontalbánP.J.M. FerrerA. ConesaA. MultiBaC: A strategy to remove batch effects between different omic data types.Stat. Methods Med. Res.202029102851286410.1177/0962280220907365 32131696
     [Google Scholar]
  52. JiangM.Z. AguetF. ArdlieK. Canonical correlation analysis for multi-omics: Application to cross-cohort analysis.PLoS Genet.2023195e101051710.1371/journal.pgen.1010517 37216410
     [Google Scholar]
  53. JeonJ. HanE.Y. JungI. MOPA: An integrative multi-omics pathway analysis method for measuring omics activity.PLoS One2023183e027827210.1371/journal.pone.0278272 36928437
     [Google Scholar]
  54. QinG. LiuZ. XieL. Multiple Omics Data Integration. In: Systems Medicine.New YorkElsevier202110311510.1016/B978‑0‑12‑801238‑3.11508‑9
     [Google Scholar]
  55. GutierrezD.B. Gant-BranumR.L. RomerC.E. An integrated, high-throughput strategy for multi-omic systems level analysis.J. Proteome Res.2018171033963408
     [Google Scholar]
  56. MarquesF.B. LealG.F. BettoniG.N. SouzadON. Integration of Bioinformatics and Clinical Data to Personalized Precision Medicine.In: ITNG 2021 18th International Conference on Information Technology-New Generations. Cham: Springer International Publishing 2021; pp. 179-8410.1007/978‑3‑030‑70416‑2_23
     [Google Scholar]
  57. GuhaP. DuttaS. MurtiK. The integration of omics: A promising approach to personalized tuberculosis treatment.Medicine in Omics20241210003310.1016/j.meomic.2024.100033
     [Google Scholar]
  58. ZhangX. KuivenhovenJ.A. GroenA.K. Forward individualized medicine from personal genomes to interactomes.Front. Physiol.2015636410.3389/fphys.2015.00364 26696898
     [Google Scholar]
  59. AhmedZ. Practicing precision medicine with intelligently integrative clinical and multi-omics data analysis.Hum. Genomics20201413510.1186/s40246‑020‑00287‑z 33008459
     [Google Scholar]
  60. ShiJ. ZhaoJ. ZhangY. Windows scanning multiomics: Integrated metabolomics and proteomics.Anal. Chem.20239551187931880210.1021/acs.analchem.3c03785 38095040
     [Google Scholar]
  61. PonziE. ThoresenM. NøstH.T. MøllersenK. Integrative, multi-omics, analysis of blood samples improves model predictions: Applications to cancer.BMC Bioinformatics202122139510.1186/s12859‑021‑04296‑0 34353282
     [Google Scholar]
  62. XuA. ChenJ. PengH. HanG. CaiH. Simultaneous interrogation of cancer omics to identify subtypes with significant clinical differences.Front. Genet.20191023610.3389/fgene.2019.00236 30984238
     [Google Scholar]
  63. ChenT. AbadiA.J. CaoL.K.A. TyagiS. multiomics: A user-friendly multi-omics data harmonisation R pipeline.F1000 Res.20211053810.12688/f1000research.53453.1
     [Google Scholar]
  64. FilippoD.M. PesciniD. GaluzziB.G. INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation.PLOS Comput. Biol.2022182e100933710.1371/journal.pcbi.1009337 35130273
     [Google Scholar]
  65. FilippoD.M. PesciniD. GaluzziG.B. INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation.PLoS Comput. Bio.2021182e100933710.1101/2021.08.13.456220
     [Google Scholar]
  66. CiaramellaA. NardoneD. StaianoA. Data integration by fuzzy similarity-based hierarchical clustering.BMC Bioinformatics202021S10)(Suppl. 1035010.1186/s12859‑020‑03567‑6 32838739
     [Google Scholar]
  67. MaoH. JiaM. DiM. HALO: Hierarchical causal modeling for single cell multi-omics data.bioRxiv20221-610.1101/2022.10.17.512602
     [Google Scholar]
  68. LeeB. ZhangS. PoleksicA. XieL. Heterogeneous multi-layered network model for omics data integration and analysis.Front. Genet.202010138110.3389/fgene.2019.01381 32063919
     [Google Scholar]
  69. DenisM. TadesseM.G. Evaluation of hierarchical models for integrative genomic analyses.Bioinformatics201632573874610.1093/bioinformatics/btv653 26545823
     [Google Scholar]
  70. VetranoS. BoumaG. BenschopR.J. ImmUniverse Consortium: Multi-omics integrative approach in personalized medicine for immune-mediated inflammatory diseases.Front. Immunol.202213100262910.3389/fimmu.2022.1002629 36439150
     [Google Scholar]
  71. LiC.X. ChenH. KermaniZ.N. Consensus clustering with missing labels (CCML): A consensus clustering tool for multi-omics integrative prediction in cohorts with unequal sample coverage.Brief. Bioinform.2023251bbad50110.1093/bib/bbad501 38205966
     [Google Scholar]
  72. BrièreG. DarboÉ. ThébaultP. UricaruR. Consensus clustering applied to multi-omics disease subtyping.BMC Bioinformatics202122136110.1186/s12859‑021‑04279‑1 34229612
     [Google Scholar]
  73. WangR.S. MaronB.A. LoscalzoJ. Multiomics network medicine approaches to precision medicine and therapeutics in cardiovascular diseases.Arterioscler. Thromb. Vasc. Biol.202343449350310.1161/ATVBAHA.122.318731 36794589
     [Google Scholar]
  74. GüngörB.B. Network and pathway based analysis of multi-omic data to enlighten molecular mechanisms of complex diseases.In: Sağlık Bilimlerinde İleri Araştırmalar Dergisi. Istanbul: Istanbul University 2022; 5: pp. (S-1)65-510.26650/JARHS2021‑1137390
     [Google Scholar]
  75. WangC. LueW. KaaliaR. KumarP. RajapakseJ.C. Network-based integration of multi-omics data for clinical outcome prediction in neuroblastoma.Sci. Rep.20221211542510.1038/s41598‑022‑19019‑5 36104347
     [Google Scholar]
  76. ComteB. BaumbachJ. BenisA. Network and systems medicine: position paper of the european collaboration on science and technology action on open multiscale systems medicine.Net. Sys. Med.202031679010.1089/nsm.2020.0004 32954378
     [Google Scholar]
  77. RanjbariS. ArslanturkS. Integration of incomplete multi-omics data using knowledge distillation and supervised variational autoencoders for disease progression prediction.J. Biomed. Inform.202314710451210.1016/j.jbi.2023.104512 37813325
     [Google Scholar]
  78. AcharyaD. MukhopadhyayA. A comprehensive review of machine learning techniques for multi-omics data integration: Challenges and applications in precision oncology.Brief. Funct. Genomics202423554956010.1093/bfgp/elae013 38600757
     [Google Scholar]
  79. AguilaC.R. PupoA.N. RodríguezH.E.W. Multi-omics data integration approaches for precision oncology.Mol. Omics202218646947910.1039/D1MO00411E 35470819
     [Google Scholar]
  80. AthienitiE. SpyrouG.M. A guide to multi-omics data collection and integration for translational medicine.Comput. Struct. Biotechnol. J.20232113414910.1016/j.csbj.2022.11.050 36544480
     [Google Scholar]
  81. HeoY.J. HwaC. LeeG.H. ParkJ.M. AnJ.Y. Integrative multi-omics approaches in cancer research: From biological networks to clinical subtypes.Mol. Cells202144743344310.14348/molcells.2021.0042 34238766
     [Google Scholar]
  82. KaramanD.E. IşıkZ. Multi-omics data analysis identifies prognostic biomarkers across cancers.Med. Sci.20231134410.3390/medsci11030044 37489460
     [Google Scholar]
  83. VahabiN. MichailidisG. Unsupervised multi-omics data integration methods: A comprehensive review.Front. Genet.20221385475210.3389/fgene.2022.854752 35391796
     [Google Scholar]
  84. DemirelH.C. AriciM.K. TuncbagN. Computational approaches leveraging integrated connections of multi-omic data toward clinical applications.Mol. Omics202218171810.1039/D1MO00158B 34734935
     [Google Scholar]
  85. DeGroatW. AbdelhalimH. PatelK. MendheD. ZeeshanS. AhmedZ. Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine.Sci. Rep.2024141110.1038/s41598‑023‑50600‑8 38167627
     [Google Scholar]
  86. WuY. XieL. AI-driven multi-omics integration for multi-scale predictive modeling of causal genotype-environment-phenotype relationships.arXiv:24070640520241-6
     [Google Scholar]
  87. BenkiraneH. PradatY. MichielsS. CournèdeP.H. CustOmics: A versatile deep-learning based strategy for multi-omics integration.PLOS Comput. Biol.2023193e101092110.1371/journal.pcbi.1010921 36877736
     [Google Scholar]
  88. ItaiY. RappoportN. ShamirR. Integration of gene expression and DNA methylation data across different experiments.Nucleic Acids Res.202351157762777610.1093/nar/gkad566 37395437
     [Google Scholar]
  89. JiY DuttaP DavuluriR Deep multi-omics integration by learning correlation-maximizing representation identifies prognostically stratified cancer subtypes.Bioinformat Adv202331vbad07510.1093/bioadv/vbad07537424943
     [Google Scholar]
  90. ChiericiM. BussolaN. MarcoliniA. Integrative network fusion: A multi-omics approach in molecular profiling.Front. Oncol.202010106510.3389/fonc.2020.01065 32714870
     [Google Scholar]
  91. GarmireL.X. Strategies to integrate multi-omics data for patient survival prediction.arXiv preprint arXiv:20081245520201-6
     [Google Scholar]
  92. ShenS. Editorial: Integrative approaches to analyze cancer based on multi‐omics.Front. Genet.202213105740810.3389/fgene.2022.1057408 36324507
     [Google Scholar]
  93. WessM. AndersenM.K. Midtbust Elise. et al. Spatial integration of multi-omics data using the novel multi-omics imaging integration toolset.bioRxiv20241-610.1101/2024.06.11.598306
     [Google Scholar]
  94. ZhouL ZhuZ WangX GaoH 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. 505-51110.1109/ICUS58632.2023.10318469
     [Google Scholar]
  95. KoenN. PreezD.I. LootsD.T. Metabolomics and personalized medicine.Front. Genet.2016537810.1016/bs.apcsb.2015.09.003
     [Google Scholar]
  96. ZhangX. WangP. QinJ. Editorial: Enhanced biological mechanism study, drug discovery and individualized medicine with single-cell multiomics data and integrative analysis.Front. Genet.202314123612610.3389/fgene.2023.1236126 37434951
     [Google Scholar]
  97. OwG.S. TangZ. KuznetsovV.A. Big data and computational biology strategy for personalized prognosis.Oncotarget2016726402004022010.18632/oncotarget.9571 27229533
     [Google Scholar]
  98. IvanisevicT. SewduthR.N. Multi-omics integration for the design of novel therapies and the identification of novel biomarkers.Proteomes20231143410.3390/proteomes11040034 37873876
     [Google Scholar]
  99. TangL ZengX. #2347 Multi-omics analysis identifies traditional Chinese medicine targets for chronic kidney disease.Nephrol Dial Transplant202439Supplement_1gfae069-071710.1093/ndt/gfae069.717
     [Google Scholar]
  100. WangL. LiY. LinS. Multi-omics identify serotonin transporter as a promising therapeutic target for essential tremor.bioRxiv20241-610.1101/2024.03.18.585649
     [Google Scholar]
  101. MoniM.A. LiòP. How to build personalized multi-omics comorbidity profiles.Front. Cell Dev. Biol.201532810.3389/fcell.2015.00028 26157799
     [Google Scholar]
  102. HameedH FaheemS ZamanM. Multiomics approaches in cancer.Elsevier: Biological Insights of Multi-Omics Technologies in Human Diseases. 2024; pp. 53-7210.1016/B978‑0‑443‑23971‑7.00003‑1
     [Google Scholar]
  103. MartinsY. Multi-task analysis of gene expression data on cancer public datasets.medRxiv20231-610.1101/2023.09.27.23296213
     [Google Scholar]
  104. BansalB. SahooA. Multi-omics data fusion using adaptive GTO guided Non-negative matrix factorization for cancer subtype discovery.Comput. Methods Programs Biomed.202322810724610.1016/j.cmpb.2022.107246 36434961
     [Google Scholar]
  105. ÇekerD.D. BaysungurV. EvmanS. LUNGBANK: A novel biorepository strategy tailored for comprehensive multiomics analysis and P-medicine applications in lung cancer.Turk. J. Biol.202448320321710.55730/1300‑0152.2696 39050710
     [Google Scholar]
  106. WangC. LyeX. KaaliaR. KumarP. RajapakseJ.C. Deep learning and multi-omics approach to predict drug responses in cancer.BMC Bioinformatics202222S10)(Suppl. 1063210.1186/s12859‑022‑04964‑9 36443676
     [Google Scholar]
  107. SethiY. PatelN. KakaN. Precision medicine and the future of cardiovascular diseases: A clinically oriented comprehensive review.J. Clin. Med.2023125179910.3390/jcm12051799 36902588
     [Google Scholar]
  108. ChouvardaI. MaglaverasN. Title Cardiovascular Big Data Analytics. In: Cardiovascular Computing—Methodologies and Clinical Applications Series in BioEngineering.SingaporeSpringer201930331310.1007/978‑981‑10‑5092‑3_15
     [Google Scholar]
  109. HanY. ZengX. HuaL. The fusion of multi-omics profile and multimodal EEG data contributes to the personalized diagnostic strategy for neurocognitive disorders.Microbiome20241211210.1186/s40168‑023‑01717‑5 38243335
     [Google Scholar]
  110. EkvallS. WestermarckT. HaviaM. AtroshiF. Personalized Management of Selected Neurological Disorders. In: Personalized Medicine, in Relation to Redox State, Diet and Lifestyle.London202010.5772/intechopen.92002
     [Google Scholar]
  111. XuZ. LiW. DongX. Precision medicine in colorectal cancer: Leveraging multi-omics, spatial omics, and artificial intelligence.Clin. Chim. Acta202455911968610.1016/j.cca.2024.119686 38663471
     [Google Scholar]
  112. MorenoC. LauraM. Multi-omics quality assessment in personalized medicine through eatris.bioRxiv20231010.1101/2023.10.25.563912
     [Google Scholar]
  113. ShabaE. VantaggiatoL. GoverniniL. Multi-omics integrative approach of extracellular vesicles: A future challenging milestone.Proteomes20221021210.3390/proteomes10020012
     [Google Scholar]
  114. ZhuX. YaoQ. YangP. Multi-omics approaches for in-depth understanding of therapeutic mechanism for Traditional Chinese Medicine.Front. Pharmacol.202213103105110.3389/fphar.2022.1031051 36506559
     [Google Scholar]
/content/journals/cbio/10.2174/0115748936360644250127095005
Loading
Integrative Multi-Omics Approaches for Personalized Medicine and Health
Curr Bioinform 21, 109 (2026); https://doi.org/10.2174/0115748936360644250127095005
/content/journals/cbio/10.2174/0115748936360644250127095005
/content/journals/cbio/10.2174/0115748936360644250127095005
Loading

Data & Media loading...


  • Article Type:     Review Article
Keyword(s): genomics; metabolome screens; Multi-omics integration; personalized medicine; proteomics; transcriptomics

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test