Machine Learning Predictions for Single-Cell Protein Expression

624.8 KB of correlation values comparing machine learning predictions of protein expression across datasets. The data, published by Josephine Fisher under a CC-BY-4.0 license, was last updated on June 4, 2026. It serves as supplementary material for a study on using predictions as a proxy for single-cell protein measurements.

Use Cases

• Benchmarking prediction algorithms based on cross-dataset correlation values.
• Validating machine learning models as proxies for single-cell protein expression.
• Comparing the performance of different predictive features across biological datasets.

Strengths

• Data is openly licensed under CC-BY-4.0 for reuse.
• File size of 624.8 KB indicates a focused, manageable dataset.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

figshare

Collection Method

Likely generated as supplementary results for a research publication.

Freshness

Last updated 2026-06-04 04:28:00; freshness should be verified.