Supplementary Material 7 from a study titled 'Integrated weighted gene co-expression network analysis and machine learning analysis identifies SEC14L5 as a potential biomarker for polycystic ovary syndrome'. The dataset is a 1.8 MB CSV file published on figshare by author Zhe Wang under a CC-BY-4.0 license and last updated on 2026-04-15.
Use Cases
- Validate the SEC14L5 biomarker candidate through independent analysis (inferred from domain, verify after download)
- Train machine learning models for polycystic ovary syndrome classification using gene expression features (inferred from domain, verify after download)
- Perform secondary network analysis on the provided gene co-expression data (inferred from domain, verify after download)
Strengths
- Published on figshare with a clear CC-BY-4.0 open license.
- File size is 1.8 MB, indicating a manageable download and processing scale.
- Last update timestamp is explicitly provided: 2026-04-15 03:31:38.
Limitations
- Metadata is minimal; actual content requires verification after download.
- Row count and column definitions are unknown, which may limit suitability assessment.
- The description 'Supplementary Material 7' provides no detail on the specific data structure or variables.
Provenance
- Source
- figshare
- Collection Method
- Likely generated as supplementary data from a computational biology study.
- Time Range
- null
- Freshness
- Last updated 2026-04-15 03:31:38; freshness should be verified.
- Geography
- null