Version 1
: Received: 2 September 2024 / Approved: 2 September 2024 / Online: 3 September 2024 (07:30:18 CEST)
How to cite:
Desta, G. M.; Aserese, M. B. Revolutionizing Personalized Medicine: Synergy with Multi-Omics Data Generation, Main Hurdles, and Future Perspectives. Preprints2024, 2024090196. https://doi.org/10.20944/preprints202409.0196.v1
Desta, G. M.; Aserese, M. B. Revolutionizing Personalized Medicine: Synergy with Multi-Omics Data Generation, Main Hurdles, and Future Perspectives. Preprints 2024, 2024090196. https://doi.org/10.20944/preprints202409.0196.v1
Desta, G. M.; Aserese, M. B. Revolutionizing Personalized Medicine: Synergy with Multi-Omics Data Generation, Main Hurdles, and Future Perspectives. Preprints2024, 2024090196. https://doi.org/10.20944/preprints202409.0196.v1
APA Style
Desta, G. M., & Aserese, M. B. (2024). Revolutionizing Personalized Medicine: Synergy with Multi-Omics Data Generation, Main Hurdles, and Future Perspectives. Preprints. https://doi.org/10.20944/preprints202409.0196.v1
Chicago/Turabian Style
Desta, G. M. and Molalegne Bitew Aserese. 2024 "Revolutionizing Personalized Medicine: Synergy with Multi-Omics Data Generation, Main Hurdles, and Future Perspectives" Preprints. https://doi.org/10.20944/preprints202409.0196.v1
Abstract
The field of personalized medicine is undergoing a transformative shift through the integration of multi-omics data, which encompasses genomics, transcriptomics, proteomics, and metabolomics. This synergy allows for a comprehensive understanding of individual health by analyzing genetic, molecular, and biochemical profiles. The generation and integration of multi-omics data enable more precise and tailored therapeutic strategies, improving the efficacy of treatments and reducing adverse effects. However, several challenges hinder the full realization of personalized medicine. Key hurdles include the complexity of data integration across different omics layers, the need for advanced computational tools, and the high cost of comprehensive data generation. Additionally, issues related to data privacy, standardization, and the need for robust validation in diverse populations remain significant obstacles. Looking ahead, the future of personalized medicine promises advancements in technology and methodologies that will address these challenges. Emerging innovations in data analytics, machine learning, and high-throughput sequencing are expected to enhance the integration of multi-omics data, making personalized medicine more accessible and effective. Collaborative efforts among researchers, clinicians, and industry stakeholders are crucial to overcoming these hurdles and fully harnessing the potential of multi-omics for individualized healthcare.
Medicine and Pharmacology, Medicine and Pharmacology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
