Recent Approaches to Integrate Multiomics Data on System Biology

Abstract

Background: In this review an overview of the development of omics studies in recent years were considered. The bottom-up and top-down approaches on system biology were discussed and some of the recent tools that integrate multiomics data on system biology studies were discussed. The improvements on omics applications from genomics to metabolomics was inspected in the light of breast cancer biomarker discovery. Results: It is known that genome or proteome analysis alone is not sufficient to elucidate the molecular mechanism of any disease, but instead, a holistic evaluation including metabolomics studies is rational and gives definite results. This review focused to present the studies performed on breast cancer biomarker development. Breast cancer markers approved in the beginning of the millennium encouraged researchers to search for diagnostic and prognostic biomarkers with the improvements of advanced analytical strategies. The recent developments on omics techniques and tools to process multi omics data are capable of presenting valuable scientific information better than ever before. Conclusion: The clinical perspective of the researches using omics techniques might be supported with the integration of multiomics data to validate the results and to understand the molecular pathway interacted in the biological system.

Advances of multi-omics applications in hepatic precancerous lesions and hepatocellular carcinoma: The role of extracellular vesicles

“…Systems biology, as a rookie in the post-genomic era, is becoming a more sophisticated approach to integromices. This approach, characterized by the integration of systems theory and experimental and computational methods, combines experimental data at multiple molecular levels with computational models and treats the system as a whole, providing a new perspective for revealing the role of EVs in human health and disease ( Gézsi et al, 2019 ; Çelebier and Ercan, 2021 ). The integrated analysis of the interactions of different molecules in the genome, transcriptome, proteome, metabolome, etc., helps to identify the best biomarker candidates.…”