Machine Learning-Determined Sex-Related Bladder Cancer Biomarkers from RNA-Seq Data

Data Sheet 8_Machine learning-based determination of sex-related bladder cancer biomarkers.csv contains gene panels identified through machine learning analysis of gender-stratified RNA-seq data. The dataset was created by Joseph R. Pizzi and last updated on 2026-04-29. It includes results from four feature selection methods, with male and female-specific gene panels achieving areas under the ROC curve of 0.932 and 0.914, respectively.

Use Cases

• Training classifiers for bladder cancer detection based on sex-specific gene expression signatures.
• Investigating biological pathways like PI3K-AKT signaling and extracellular matrix reorganization in bladder cancer.
• Validating potential high-impact biomarkers such as PRAC1, PCDH11Y, and androgen receptor-related genes in external datasets.

Strengths

• Performance metrics are provided, with male and female-specific panels achieving AUCs of 0.932 and 0.914.
• Results are derived from four distinct machine learning feature selection methods, including recursive feature elimination and random forest.
• The dataset is openly licensed under CC-BY-4.0.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is small at 15.0 KB, indicating limited scope, likely containing summary results rather than raw expression data.

Provenance

Source

figshare

Collection Method

Machine learning analysis of gender and disease-stratified RNA-seq data using differential expression analysis, recursive feature elimination, logistic regression, and random forest.

Freshness

Last updated 2026-04-29 05:58:40; freshness should be verified.