Multi-omics data integration and analysis pipeline for precision medicine: Systematic review.

Area of Science:

• Genomics and Bioinformatics
• Translational Medicine
• Computational Biology

Background:

• The limitations of single-omics approaches in capturing biological complexity have hindered advancements in precision medicine.
• The multi-omics approach, integrating data from DNA sequencing, RNA sequencing, and mass spectrometry, offers a more holistic view of biological systems.
• Existing reviews often focus on specific aspects of multi-omics analysis, lacking a comprehensive overview of the entire pipeline.

Purpose of the Study:

• To provide a comprehensive overview of the multi-omics data analysis pipeline for precision medicine.
• To cover databases, dimensionality reduction, data integration techniques, and downstream applications.
• To discuss evaluation metrics, model interpretability, and future challenges in multi-omics integration.

Main Methods:

• Review of popular multi-omics databases.
• Exploration of dimensionality reduction techniques.
• Detailed examination of data integration methods and their applications.
• Discussion of evaluation metrics and model interpretability.

Main Results:

• Identification of key multi-omics databases and computational methods.
• Categorization of data integration strategies and their relevance to downstream analyses like cancer classification and biomarker discovery.
• Highlighting the importance of model interpretability and evaluation metrics for robust multi-omics studies.

Conclusions:

• A comprehensive understanding of the multi-omics pipeline is crucial for advancing precision medicine.
• Addressing challenges in data integration and interpretability will enhance the clinical utility of multi-omics approaches.
• Future research should focus on developing standardized, interpretable, and robust multi-omics integration methodologies.