Multiple Sclerosis Biomarker Data for Diagnostic Model Development

A pilot study dataset by Tobias Hegelmaier, published on figshare in 2026, integrating inflammatory and neurodegenerative biomarkers to classify multiple sclerosis subtypes. The dataset likely contains measurements from retinal imaging, serum protein levels, and immunophenotyping. It is a small dataset of 17.4 KB, stored in a DOCX file.

Use Cases

• Training classification models to distinguish relapsing from secondary progressive MS based on biomarker combinations.
• Analyzing the relationship between retinal layer thickness and disease progression.
• Investigating the predictive power of serum neurofilament light chain (sNfL) and glial fibrillary acidic protein (GFAP) levels.
• Exploring correlations between monocyte subset frequencies and clinical MS categories.

Strengths

• Integrates multimodal biomarker data from retinal imaging, serum analysis, and immunophenotyping.
• The associated model achieved approximately 80% accuracy in distinguishing MS subtypes.
• Published under a permissive CC-BY-4.0 license for reuse.

Limitations

• Row count and column-level documentation are unknown, limiting suitability assessment.
• The dataset is very small (17.4 KB), suggesting a limited sample size from a pilot study.
• Freshness should be verified as the last update date is in the future (2026-05 -20).

Provenance

Source

figshare, author Tobias Hegelmaier

Collection Method

Clinical study employing multimodal assessment including multifocal electroretinography, optical coherence tomography, serum multiplex assays, and immunophenotyping.

Time Range

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Freshness

Last updated 2026-05-20 04:32:53

Geography

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