Supplementary File 2: Multi-omics Biomarker Data for Myocardial Infarction

A 804.6 KB PDF file published on figshare by Jiacheng Wu on 2026-04 13. The dataset contains results from a multi-omics and machine learning study to identify key genes as potential biomarkers for Acute Myocardial Infarction (AMI). The analysis integrated GWAS and eQTL data with three gene expression microarray datasets from the GEO database.

Use Cases

• Validate potential gene biomarkers for myocardial infarction based on the identified key genes like LIPA and PECAM1.
• Train machine learning models for disease prediction based on the described multi-omics integration approach.
• Conduct functional enrichment analysis based on the GO and KEGG pathway results mentioned.
• Perform immunoinfiltration analysis based on the described correlations with immune cell types.

Strengths

• Data is derived from multiple established public sources, including FinnGen, GTEx, and three GEO datasets.
• Results were validated using RT-qPCR and western blot, as described in the methods.
• The dataset is openly available under a CC-BY-4.0 license.

Limitations

• The primary data file is a PDF (804.6 KB), which likely contains summarized results rather than raw, machine-readable data tables.
• Column-level documentation and row count are unknown, limiting suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare, with data sourced from FinnGen, GTEx, and GEO databases.

Collection Method

Integrated analysis of GWAS, eQTL, and gene expression microarray data using TWAS, machine learning, and Bayesian colocalization.

Time Range

The temporal coverage of the underlying source datasets is not specified.

Freshness

Last updated 2026-04-13 05:20:48; freshness should be verified.

Geography

The geographic coverage of the underlying source datasets is not specified.