DNA Damage Response Prediction Model Performance Metrics

Alejandro Leyva's 5.5 KB Excel file contains performance metrics for a cell-level DNA damage response prediction model. The data, last updated in May 2026, reports results from a five-fold cross-validation procedure. Metrics likely include Pearson correlation, Spearman correlation, mean absolute error, mean squared error, and coefficient of determination.

Use Cases

• Benchmarking machine learning models for DDR prediction based on reported correlation and error metrics.
• Comparing the performance of different model architectures using the provided cross-validation results.
• Analyzing the reliability of a DDR predictor based on the coefficient of determination and correlation scores.

Strengths

• Provides five distinct performance metrics (Pearson, Spearman, MAE, MSE, R²) for a detailed evaluation.
• Results are derived from a five-fold cross-validation, suggesting a standard methodology for assessing model generalizability.
• Dataset is openly available under a CC-BY-4.0 license, permitting reuse with attribution.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for certain statistical analyses.
• The dataset is very small at 5.5 KB, indicating it contains only summary metrics, not the underlying raw or feature data.

Provenance

Source

figshare, author Alejandro Leyva.

Collection Method

Likely contains aggregated results from a computational modeling experiment.

Time Range

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Freshness

Last updated 2026-05-04 17:49:02; freshness should be verified.

Geography

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