Malaria Parasite var Gene Location Predictions from Machine Learning Models

5.5 KB Excel file contains the overall accuracy results for a 'positional' classifier predicting the chromosomal location of var genes in the malaria parasite Plasmodium falciparum. The data was generated by author Elcid Aaron Pangilinan using machine learning models on a reannotated subset of var genes classified as upsA, upsE, upsB_subtelomeric, and upsBC_internal. The dataset was last updated on April 16, 2026, and is shared under a CC-BY-4.0 license.

Use Cases

• Benchmarking machine learning model performance for gene location prediction based on the reported accuracy metric.
• Analyzing the predictive power of classifiers for internal versus subtelomeric var gene locations.
• Comparing classification outcomes across different var gene promoter types (upsA, upsE, upsB, upsC).
• Supporting methodological validation for genomic sequence classification pipelines.

Strengths

• Dataset is small (5.5 KB), facilitating quick download and inspection.
• Provides a specific accuracy metric for a defined machine learning task.
• Focuses on a reannotated and categorized subset of var genes, suggesting curated input data.
• Uses a permissive CC-BY-4.0 license for open reuse.

Limitations

• Description metadata is limited; actual data quality and structure require manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred from the Excel file.
• Row count is unknown, which may limit suitability assessment for statistical analysis.

Provenance

Source

figshare

Collection Method

Likely contains results from machine learning models applied to genomic data.

Time Range

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Freshness

Last updated 2026-04-16 17:47:51; freshness should be verified.

Geography

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