multiomics

Byaibio

Multiomics is an integrated molecular profiling approach that combines genomic, epigenomic, transcriptomic, proteomic, and metabolomic signals to capture biology across multiple layers at once. Its primary function is to improve early detection and minimal residual disease (MRD) monitoring by identifying biomarker patterns that single-omics assays may miss. It is being applied in colorectal cancer liquid biopsy, prostate cancer risk stratification beyond PSA screening, and cardiovascular disease to resolve molecular signatures of lipoprotein(a)-driven disease. The approach also extends to microbiome research through integrated genomic, proteomic, and metabolomic datasets, and is supported by cross-omics bioinformatics methods and the global study framework. Recent literature highlights multiomics as a biomarker discovery strategy for early cancer detection and monitoring, with review-level evidence emphasizing its role in African and Middle Eastern prostate cancer populations and in lipoprotein(a)-associated cardiovascular disease. Overall, multi-omics is increasingly used as a systems-level diagnostic technology to integrate 5 major molecular classes and strengthen precision medicine workflows.

Cancer

  • Integrated multiomics liquid biopsy was used for biomarker discovery in early detection and monitoring of colorectal cancer (PMID:41930252).
  • A 2026 Journal of Proteome Research study discussed multiomics and AI beyond PSA screening to improve prostate cancer detection and risk stratification (PMID:41894385).
  • The same prostate cancer review focused on African and Middle Eastern patient populations, highlighting the need for more informative molecular profiling beyond PSA alone (PMID:41894385).
  • Multiomics integration was explicitly framed as a strategy to enhance prostate cancer detection and risk stratification (PMID:41894385).

Cardiovascular Disease

  • Multiomics profiling was used to unravel molecular signatures of lipoprotein(a)-driven cardiovascular disease (PMID:41908166).
  • A 2026 review in Global Heart summarized emerging multiomics data on lipoprotein(a)-driven cardiovascular disease (PMID:41908166).
  • The integrated approach supports disease characterization by combining genomic, epigenomic, transcriptomic, proteomic, and metabolomic layers (PMID:41908166).
  • This application reflects the broader use of multiomics for biomarker discovery in complex, multifactorial disease states (PMID:41908166).

Microbiome and Computational Biology

  • An integrated dataset framework combining genomic, proteomic, and metabolomic data was applied in microbiome studies (PMID:41418718).
  • The 2026 Computational Biology and Chemistry paper traced the evolution from 16S rRNA analysis to deep learning in human microbiome research, placing multiomics within modern computational workflows (PMID:41418718).
  • Cross-omics integration was supported by bioinformatics methods in the review context (PMID:41418718).
  • The global study framework was noted as utilizing multiomics profiling, indicating broader translational use beyond single-disease settings (PMID:41418718).