Table 9_Exploration of candidate genes associated with rare SNVs in pulmonary stenosis usi

A genomic analysis dataset from a study of 185 sporadic pulmonary stenosis patients and 100 healthy controls. The data was generated by Yuting Liu and last updated in June 2026. It contains prioritized candidate genes identified through whole-exome sequencing, gene-level burden tests, and three machine learning algorithms.

Use Cases

• Prioritizing candidate disease genes based on rare variant burden tests and machine learning feature selection.
• Comparing the performance of LASSO, Random Forest, and XGBoost algorithms for feature selection in genomic data.
• Validating candidate gene lists through protein-protein interaction network analysis.
• Investigating the expression of prioritized genes, such as HAND2 or NOTCH3, in relevant cell types like human pulmonary artery endothelial cells.

Strengths

• Data is derived from whole-exome sequencing of 185 patients and 100 controls, providing a case-control study design.
• Analysis employed three distinct machine learning algorithms (LASSO, RF, XGBoost) and conventional statistical tests for cross-validation.
• A final list of 17 specific candidate genes, including HAND2 and NOTCH3, is provided.

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 very small at 21.5 KB, indicating limited scope, likely containing summary results rather than raw sequencing data.

Provenance

Source

figshare

Collection Method

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

Freshness

Last updated 2026-06-04 22:01:41; freshness should be verified.