Supplementary File 5: Multi-Omics Data for Atherosclerosis Biomarker Discovery

A 1.2 MB PDF file published on figshare in May 2026 by Yirong Ma. This supplementary file contains multi-omics data from a study integrating proteomics, transcriptomics, and network analysis to identify lactylation-linked biomarkers for atherosclerosis. The research involved experimental validation in a mouse model and computational screening for potential therapeutics.

Use Cases

• Training diagnostic machine learning models based on the 25 candidate biomarker genes identified in the study.
• Analyzing immune cell infiltration patterns in atherosclerosis using the CIBERSORT and single-cell data mentioned.
• Prioritizing therapeutic compounds for experimental validation based on the Connectivity Map (CMap) analysis results.
• Studying the role of lactylation in vascular inflammation and disease pathways as described in the background.

Strengths

• Includes data from multiple omics layers: proteomics (472 differentially expressed proteins), transcriptomics (2,544 DEGs), and network analysis (2,059 module genes).
• Biomarker candidates were validated using an ensemble of 12 machine-learning algorithms, achieving a reported mean AUC of 0.979.
• Core findings were experimentally validated in an Apoe−/− mouse model using RT-qPCR, Western blot, and immunofluorescence.

Limitations

• The dataset is provided as a PDF file, which may require extraction and parsing to access structured data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for specific computational tasks.

Provenance

Source

figshare

Collection Method

Generated from a study modeling atherosclerosis in HUVECs and Apoe−/− mice, integrating public transcriptomic data (GEO) with proprietary proteomics.

Time Range

The study's temporal coverage is not specified.

Freshness

Last updated 2026-05 08 05:52:43.

Geography

The geographic coverage of the data is not specified.