Developing a 'personalome' for precision medicine: emerging methods that compute interpretable effect sizes from...

Area of Science:

• Bioinformatics
• Computational Biology
• Translational Informatics

Background:

• Precision medicine requires analyzing individual -omics data, but current methods are population-focused.
• Interpreting single-subject -omics, especially non-sequence data, is underdeveloped.
• Novel computational approaches are needed to capture individual variability.

Purpose of the Study:

• To survey emerging computational and translational informatics methods for constructing single-subject personalomes.
• To identify methods for predicting clinical outcomes and therapeutic responses using personalome data.
• To emphasize methods providing interpretable readouts for biomedical researchers.

Main Methods:

• Review of computational and translational informatics methods.
• Focus on methods for analyzing single-subject -omics data (transcriptome, proteome, metabolome).
• Emphasis on methods validated through simulations, cross-validations, or retrospective datasets.

Main Results:

• Single-subject transcriptome analytics show the most development.
• All surveyed methods were validated using robust approaches.
• A growing field offers opportunities for developing new validation methods.

Conclusions:

• Novel computational methods are crucial for advancing precision medicine through personalome analysis.
• Future studies should focus on integrating multiple -omics data for comprehensive personalome interpretation.
• Interpreting personalomes can provide valuable insights into individual patient outcomes and treatments.