Machine learning techniques identified sex-specific gene panels for bladder cancer diagnosis. Joseph R. Pizzi published this 19.4 KB CSV dataset on figshare in April 2026. Male and female-specific panels achieved AUC scores of 0.932 and 0.914 respectively on unseen data.
Use Cases
- Training classifiers for sex-specific bladder cancer diagnosis based on identified gene panels.
- Investigating pathways like PI3K-AKT signaling and extracellular matrix reorganization linked to sex differences.
- Evaluating candidate biomarkers such as PRAC1, PCDH11Y, AR, PLXNA1, USP54, and PMEPA1 for experimental validation.
Strengths
- Performance metrics are reported: male panel AUC 0.932, female panel AUC 0.914.
- The dataset is openly licensed under CC-BY-4.0.
- The methodology is described, using four feature selection methods on RNA-seq data.
Limitations
- Row count is unknown, which may limit suitability assessment.
- Column-level documentation is absent; field semantics must be inferred after download.
- The dataset is very small (19.4 KB), indicating limited scope.
Provenance
- Source
- figshare
- Collection Method
- Machine learning analysis of gender and disease-stratified RNA-seq data.
- Freshness
- Last updated 2026-04-29 05:58:42; freshness should be verified.