2NPLGBM: Supplementary Results for Genomic Prediction Model Validation

Supplementary Excel data supporting a genomic prediction model that merges classical and machine learning methods. The file contains 10 sheets with results from hyperparameter tuning, multiple validation schemes, and selection efficiency analyses. It was authored by Bright Enogieru Osatohanmwen and last updated on May 31, 2026.

Use Cases

• Compare the performance of different cross-validation schemes based on the 'Prediction ability and selection efficiency' sheets.
• Analyze the impact of hyperparameter tuning on model outcomes based on the 'Hyperparameters tuned with Bayesian optimization' sheet.
• Evaluate selection efficiency at different thresholds (5%, 10%, 20%, 30%) based on the dedicated sheet.
• Assess variance components and heritability estimates for traits based on Sheet 7.

Strengths

• Contains results from multiple validation schemes (LOYO, RW, 5-Fold, Tester_CVO, Tester_CVOO) for comparative analysis.
• Includes selection efficiency metrics at four different thresholds (5%, 10%, 20%, 30%).
• File size is 262.3 KB, making it quick to download and inspect.

Limitations

• Row count and column-level details are unknown, limiting suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

figshare

Collection Method

Likely generated as supplementary material for a research paper on genomic prediction.

Freshness

Last updated 2026-05-31 03:22:42; freshness should be verified.