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

Data Sheet 10 contains gene panels identified as potential sex-specific biomarkers for bladder cancer. The dataset was created by Joseph R. Pizzi and last updated on 2026-04-29. It is a small CSV file (15.0 KB) resulting from a machine learning analysis of gender and disease-stratified RNA-seq data.

Use Cases

• Training classifiers to distinguish bladder cancer from non-tumor samples based on identified gene panels.
• Investigating sex-specific biological pathways in cancer, such as PI3K-AKT signaling or extracellular matrix reorganization.
• Validating high-impact predictor genes like PRAC1, PCDH11Y, or androgen receptor-related genes in external datasets.
• Building a framework for constructing high-performance, compact gene panels (50 genes or less) for diagnostic applications.

Strengths

• Gene panels achieved high performance, with reported AUCs of 0.932 (male) and 0.914 (female) on unseen data.
• Results are derived from applying four distinct machine learning feature selection methods to RNA-seq data.
• Dataset is openly licensed under CC-BY-4.0, facilitating reuse and sharing.

Limitations

• Row count and column-level documentation are unknown, which limits suitability assessment.
• The dataset is very small (15.0 KB), suggesting it contains summary results rather than raw expression data.
• Freshness should be verified as the last update date is in the future (2026-04-29).

Provenance

Source

Joseph R. Pizzi via figshare.

Collection Method

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

Freshness

Last updated 2026-04-29 05:58:39.