Omics Data Preprocessing for Machine Learning: A Case Study in Childhood Obesity.

Area of Science:

• Biomedical research
• Computational biology
• Genomics and bioinformatics

Background:

• Machine learning (ML) is increasingly used for disease outcome prediction using omics data.
• Effective ML model construction relies heavily on proper data pre-processing and algorithm selection.
• Current ML approaches often falter in experimental design, feature selection, and data management for omics data.

Purpose of the Study:

• To provide a comprehensive guideline for applying ML to multi-omics human data.
• To present best practices and recommendations for key steps in ML-driven omics research.
• To address challenges in multi-omics data analysis for disease prediction.

Main Methods:

• Review and synthesis of best practices for ML in multi-omics research.
• Detailed description of pre-processing strategies for various omics data types.
• Illustrative examples using real-world multi-omics data to address common challenges.

Main Results:

• Identification of common pitfalls in ML application to omics data (experimental design, feature selection, pre-processing, algorithm choice).
• Presentation of specific recommendations for handling multi-omics data challenges like heterogeneity, noise, high dimensionality, missing values, and class imbalance.
• Demonstration of effective strategies using real data examples.

Conclusions:

• Adherence to best practices in data handling and algorithm selection is crucial for successful ML-based disease prediction from multi-omics data.
• The proposed guideline offers a framework to overcome inherent challenges in multi-omics research.
• Future work can build upon these findings for enhanced predictive model development.