Cell-Level Apoptosis Prediction Model Performance Metrics

Performance metrics for a cell-level apoptosis prediction model, evaluated using five-fold cross-validation. The dataset includes Pearson correlation, Spearman correlation, mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R²). It was authored by Alejandro Leyva and last updated on May 4, 2026.

Use Cases

• Benchmarking apoptosis prediction models based on reported correlation and error metrics.
• Comparing the performance of different machine learning algorithms for cell-level analysis based on cross-validation results.
• Validating model generalizability in biological image analysis based on the five-fold cross-validation framework.

Strengths

• Performance is reported across five-fold cross-validation, which suggests a method for assessing model generalization.
• Includes five standard regression evaluation metrics: Pearson correlation, Spearman correlation, MAE, MSE, and R².
• Dataset is shared under a permissive CC-BY-4.0 license.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

figshare

Freshness

Last updated 2026-05-04 17:49:01; freshness should be verified.