Random forests for genomic data analysis.

Area of Science:

• Genomics
• Machine Learning
• Bioinformatics

Background:

• Genomic data analysis presents challenges due to high dimensionality.
• Random Forests (RF) are robust ensemble methods suitable for 'large p, small n' problems.
• RF can capture feature correlations and interactions, crucial for genomic insights.

Purpose of the Study:

• To systematically review the applications of Random Forests in genomic data analysis.
• To highlight recent advancements in RF methodologies for genomics.
• To cover diverse applications from prediction to unsupervised learning.

Main Methods:

• Review of existing literature on Random Forests in genomics.
• Categorization of RF applications based on genomic tasks.
• Synthesis of recent progress and methodological developments.

Main Results:

• RF is widely applied in genomic prediction and classification.
• RF demonstrates effectiveness in variable selection for identifying key genomic markers.
• RF facilitates pathway analysis, genetic association studies, and epistasis detection.
• Unsupervised learning applications of RF in genomics are also explored.

Conclusions:

• Random Forests are a versatile and powerful tool for high-dimensional genomic data.
• RF applications continue to expand, offering significant advancements in genomic research.
• The review underscores the importance of RF in modern bioinformatics and genetic studies.