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

• Published in: Computational biology and chemistry 2024-12, Vol.113, p.108254, Article 108254
• Main Authors: Abdelaziz, Esraa Hamdi, Ismail, Rasha, Mabrouk, Mai S., Amin, Eman
• Format: Article
• Language: English
• Subjects: Computational Biology; Data integration; Dimensionality reduction; Genomics; Humans; Interpretability; Machine learning; Multi-omics; Multiomics; Precision Medicine
• ISSN: 1476-9271; 1476-928X; 1476-928X
• DOI: 10.1016/j.compbiolchem.2024.108254

Precision medicine has gained considerable popularity since the "one-size-fits-all" approach did not seem very effective or reflective of the complexity of the human body. Subsequently, since single-omics does not reflect the complexity of the human body’s inner workings, it did not result in the expected advancement in the medical field. Therefore, the multi-omics approach has emerged. The multi-omics approach involves integrating data from different omics technologies, such as DNA sequencing, RNA sequencing, mass spectrometry, and others, using computational methods and then analyzing the integrated result for different downstream analysis applications such as survival analysis, cancer classification, or biomarker identification. Most of the recent reviews were constrained to discussing one aspect of the multi-omics analysis pipeline, such as the dimensionality reduction step, the integration methods, or the interpretability aspect; however, very few provide a comprehensive review of every step of the analysis. This study aims to give an overview of the multi-omics analysis pipeline, starting with the most popular multi-omics databases used in recent literature, dimensionality reduction techniques, details the different types of data integration techniques and their downstream analysis applications, describes the most commonly used evaluation metrics, highlights the importance of model interpretability, and lastly discusses the challenges and potential future work for multi-omics data integration in precision medicine. [Display omitted] •Comprehensive Review: The paper provides a detailed overview of the multi-omics analysis pipeline, covering databases, dimensionality reduction, integration techniques, evaluation metrics, and interpretability and suggests potential improvements and challenges in the field.•Dimensionality Reduction Techniques: Explores challenges posed by the curse of dimensionality and reviews various dimensionality reduction methods, including autoencoders and PCA.•Types of Data Integration and their applications: Classifies integration methods into early, middle, mixed, and late integration, emphasizing their applications in downstream analyses.•Evaluation Metrics: Summarizes common evaluation criteria used in multi-omics studies, such as the Concordance Index (c-index) and accuracy metrics.•Interpretability and Explainability: Discusses the importance of model interpretability in clinical settings, outlining approaches for making mod