Patient-specific multi-omics models and the application in personalized combination therapy.

Area of Science:

• Biotechnology
• Genomics
• Personalized Medicine

Background:

• High-throughput technologies generate vast individual multi-omics data (genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics).
• Multi-omics applications are expanding into clinical settings.
• Combination therapy, using multiple drugs, is crucial for diseases like cancer.

Purpose of the Study:

• To review various omics platforms.
• To discuss methods for multi-omics data integration.
• To explore personalized combination therapy development.

Main Methods:

• Overview of high-throughput omics platforms.
• Discussion of data integration techniques for multi-omics datasets.
• Exploration of strategies for personalized combination therapy.

Main Results:

• Multi-omics data integration facilitates identification of novel combination therapies.
• Personalized approaches can optimize drug efficacy and reduce toxicity.
• Technological advancements support clinical translation of multi-omics insights.

Conclusions:

• Patient-specific multi-omics data integration is key to advancing personalized combination therapy.
• This approach holds significant promise for improving treatment outcomes in complex diseases.
• Further research into data integration methods is essential for clinical application.