Table 1_Exploration of candidate genes associated with rare SNVs in pulmonary stenosis usi

185 sporadic pulmonary stenosis patients and 100 healthy controls were analyzed using whole-exome sequencing. The dataset contains results from gene-level burden tests and three machine learning algorithms, leading to the prioritization of 17 candidate genes. The data was published by Yuting Liu on figshare and last updated in June 2026.

Use Cases

• Prioritizing candidate disease genes based on rare variant burden tests and machine learning results.
• Comparing feature selection patterns of LASSO, Random Forest, and XGBoost algorithms on genomic data.
• Validating candidate genes through protein-protein interaction network analysis.
• Investigating gene expression patterns in human pulmonary artery endothelial cells (HPAECs) for prioritized genes.

Strengths

• Data is derived from whole-exome sequencing of 185 patients and 100 controls.
• Analysis includes results from three distinct machine learning algorithms (LASSO, RF, XGBoost) and a burden test.
• Final candidate gene list includes 17 specific genes (e.g., HAND2, NOTCH3, COL5A1).

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The sample size of 185 patients may limit statistical power for detecting rare genetic associations.

Provenance

Source

figshare

Collection Method

Whole-exome sequencing data analyzed with pathogenicity-filtering strategies, gene-level burden tests (SKAT-O), and machine learning algorithms.

Freshness

Last updated 2026-06-04 22:01:44; freshness should be verified.