multi-omics

Byaibio

Multi-omics is an integrated analytical approach that combines multiple molecular layers—such as genomics, transcriptomics, proteomics, metabolomics, epigenomics, microbiome, and radiomics—to improve mechanistic understanding and identify novel biomarkers and drug targets. It is increasingly used in cardiovascular disease, gastrointestinal cancers, glioblastoma, breast cancer, tumor immune microenvironment, and inflammatory bowel disease, with applications spanning diagnosis, treatment-response prediction, and precision oncology. Recent literature emphasizes that integrating omics data with machine learning or artificial intelligence can strengthen biomarker discovery and clinical stratification, including in psychiatric disorders, Alzheimer’s disease, perioperative neurocognitive disorder, and uterine fibroids. In cancer, multi-omics has been used to refine subtype definitions, decode tumor heterogeneity, and analyze immunotherapy response, including combined genomics-transcriptomics-proteomics-metabolomics workflows and broader 6-layer profiling with spatial data. The approach is also being applied to microbiome-based diagnostics and to identify candidate biomarkers linked to heel bone mineral density, underscoring its role as a translational tool across complex diseases. Overall, the literature supports multi-omics as a key strategy for linking genotype to phenotype and for uncovering actionable pathways and vulnerable targets.

Cardiovascular disease

  • Integrated omics was highlighted as necessary for improving mechanistic understanding and enabling biomarker and drug target discovery in cardiovascular disease. (PMID:41819819)
  • Multi-omics approaches were discussed as part of bridging genotype, phenotype, and clinical insight in cardiovascular disease. (PMID:41819819)

Cancer and precision oncology

  • Multi-omics profiling was used to decode tumor heterogeneity and predict treatment response in breast cancer, including integrated genomics, transcriptomics, proteomics, and metabolomics. (PMID:41616992; PMID:41936855)
  • Integrated multi-omics was described as reshaping the clinical understanding and management of gastrointestinal cancers, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and spatial profiling. (PMID:41992238)
  • Multi-omics technologies were applied to study glioblastoma biology and identify actionable biomarkers and mechanistic pathways. (PMID:41921727; PMID:41587111)
  • Multi-omics approaches were described as supporting effective drug combinations and the development of therapeutics containing new vulnerable targets in targeted therapy. (PMID:41813658)

Immunotherapy and tumor microenvironment

  • Multi-omics approaches were used to investigate therapy-driven alterations in the tumor immune microenvironment. (PMID:41813658)
  • Computational modeling with multi-omics data was used for immunotherapy response prediction. (PMID:41986319)
  • Multi-omics and AI were highlighted as enabling personalized medicine in tumor immunotherapy. (PMID:41938619)
  • Integrated omics approaches were described as enabling detailed study of the dynamic interplay between therapeutic modalities and the tumor immune microenvironment. (PMID:41813658)

Diagnostics and biomarker discovery

  • Multi-omics integration was used to develop diagnostic approaches for inflammatory bowel disease, combining microbiome, metabolomic, transcriptomic, and proteomic data. (PMID:41220286)
  • Multi-omics approaches were applied for biomarker discovery in uterine fibroids, although the field remains fragmented across platforms and study designs. (PMID:41954864)
  • Multi-omics data integration with quantitative trait loci data identified candidate biomarkers associated with heel bone mineral density. (PMID:41874668)
  • Integrated analytical approaches were recommended alongside machine-learning-driven analyses for biomarker discovery. (PMID:41654009)

Neurology, psychiatry, and aging

  • Integrated data approaches were used to identify core risk genes associated with Alzheimer’s-disease. (PMID:41879435)
  • Integrated analytical approaches were proposed for combining genetic and epigenetic discoveries with other data types in perioperative neurocognitive disorder research. (PMID:41620312)
  • Multi-omics biomarkers were discussed for diagnosis and stratification in psychiatric disorders. (PMID:41655615)
  • Multi-omics was also presented as a translational strategy to strengthen gut microbiota resilience and promote healthy aging. (PMID:41843355)