Table 6_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 (LASSO, RF, XGBoost) that prioritized 17 candidate genes. The data was published by Yuting Liu on figshare under a CC-BY-4.0 license and last updated on 2026-06-04.

Use Cases

• Prioritizing candidate disease genes based on rare single nucleotide variants identified from whole-exome sequencing data.
• Comparing feature selection patterns of different machine learning algorithms (LASSO, RF, XGBoost) for genetic data analysis.
• Validating machine learning-derived gene lists against results from conventional statistical burden tests.
• Investigating protein-protein interaction networks for candidate genes associated with pulmonary stenosis.

Strengths

• Data is derived from whole-exome sequencing of 185 patients and 100 controls, providing a specific cohort size.
• Analysis employed three distinct machine learning algorithms (LASSO, RF, XGBoost) and a statistical burden test for cross-validation.
• The dataset explicitly lists 17 prioritized candidate genes, including HAND2, SETD2, and NOTCH3.

Limitations

• Row count and column-level documentation are unknown, which limits suitability assessment and requires manual inspection after download.
• The dataset is small at 41.0 KB, indicating limited scope, likely containing summary results rather than raw sequencing data.

Provenance

Source

figshare, author Yuting Liu.

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 22:01:43; freshness should be verified.