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Related Experiment Video

Updated: Mar 27, 2026

Analyses of Proteinuria, Renal Infiltration of Leukocytes, and Renal Deposition of Proteins in Lupus-prone MRL/lpr Mice

Analyses of Proteinuria, Renal Infiltration of Leukocytes, and Renal Deposition of Proteins in Lupus-prone MRL/lpr Mice

Published on: June 8, 2022

Identifying Systemic Lupus Erythematosus From Serum Proteomic Profiles Using Machine Learning and Genetic Risk

Mehmet Hocaoǧlu^1,2, Jishnu Das³, Amr H Sawalha^1,2,4,5

¹Division of Rheumatology and Clinical Immunology, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania.

Arthritis & Rheumatology (Hoboken, N.J.)

|March 26, 2026

Summary

This summary is machine-generated.

Proteomic machine learning models accurately identify lupus (systemic lupus erythematosus) and predict future cases. Novel protein biomarkers were identified, advancing lupus diagnostics.

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The bm12 Inducible Model of Systemic Lupus Erythematosus SLE in C57BL/6 Mice

The bm12 Inducible Model of Systemic Lupus Erythematosus SLE in C57BL/6 Mice

Published on: November 1, 2015

Area of Science:

Immunology
Proteomics
Machine Learning

Background:

Systemic lupus erythematosus (SLE) diagnosis relies on clinical and serological markers.
Proteome-wide risk models for SLE are not well-established.
Serum proteomic profiles offer potential for SLE identification.

Purpose of the Study:

To develop and validate proteomic classification models for SLE identification.
To compare machine learning models with linear models and polygenic risk scores (PRS).
To identify novel protein biomarkers for SLE.

Main Methods:

Utilized UK Biobank data with proteomic profiles from SLE patients and controls.
Performed differential proteomic expression analysis and hierarchical clustering.
Developed and validated linear and machine learning models, including replication in independent cohorts.

Main Results:

Machine learning models significantly outperformed linear models in identifying existing SLE and predicting future cases.
The model achieved high sensitivity (~90%) and specificity (~95%) in SLE patients, replicated across cohorts.
Identified SCARB2, SOD2, CD302, Galectin-9, and GGT5 as key proteins for SLE identification.

Conclusions:

Proteomic machine learning models demonstrate high accuracy for SLE detection and prediction.
These models show promise for early SLE diagnosis before clinical manifestation.
Novel candidate biomarkers for SLE were identified through model interpretation.