Additional file 6 of Integrated weighted gene co-expression network analysis and machine l

Supplementary Material 6 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 37.0 KB file, published on figshare by Zhe Wang under a CC-BY-4.0 license, was last updated on 2026-04-15. The content likely contains supplementary data or analysis details supporting the main research findings.

Use Cases

• Reproducing or validating the gene co-expression network analysis from the associated study (inferred from domain, verify after download)
• Using identified gene signatures as features for training new PCOS classification models (inferred from domain, verify after download)
• Benchmarking SEC14L5 or other candidate biomarkers against independent datasets (inferred from domain, verify after download)

Strengths

• Published on figshare with a clear CC-BY-4.0 license.
• Last updated date is explicitly provided: 2026-04-15 03:31:37.

Limitations

• Metadata is minimal; actual content requires verification after download.
• Row count and column definitions are unknown, which may limit suitability assessment.
• The 37.0 KB file size suggests a very limited scope, likely containing summary results or a single supplementary table.

Provenance

Source

figshare, authored by Zhe Wang.

Collection Method

Likely generated as supplementary material for a computational biology research paper.

Time Range

null

Freshness

Last updated 2026-04-15 03:31:37.

Geography

null