A clinical decision support console for Systemic Lupus Erythematosus.

A three-step machine-learning pipeline that turns blood RNA into a per-patient SLE readout.

Biomed09 · Team
Manna Berry  ·  Kiwi Lin  ·  Udit Samant  ·  Hadi Shafat  ·  Minh Hieu Tran  ·  Jillian Zhao

DATA3888 · 2026
The University of Sydney

Background and Motivations

Disease Context

SLE is complex and unpredictable

Target disease - Systemic Lupus Erythematosus (SLE) (NIAMS, 2022; MedlinePlus, 2024).
Autoimmune, multi-organ disease (NIAMS, 2022; MedlinePlus, 2024).
Symptoms vary among patients (NIAMS, 2022; NHS, 2023; MedlinePlus Medical Encyclopedia, 2025).
No single definitive test (Aringer & Bertsias, 2025).

Current Gap

Current care is still reactive

Diagnosis is difficult (Aringer & Bertsias, 2025).
Flare risk is hard to anticipate (Lin et al., 2024).
Treatment response varies (Lin et al., 2024).

Our approach

A three-stage machine learning pipeline

Same modelling workflow, different clinical questions.

01Diagnosis

Does this patient likely have SLE?

02Flare Risk

Is the disease likely to worsen or flare?

03Treatment Response

How likely is the patient to respond to treatment?

Method · how each model stage is done

Three model stages, one Random Forest framework.

Model 1
See how

Diagnosis

"Does this patient have lupus?"

Data GSE72509 · 117 whole-blood RNA-seq samples (99 SLE, 18 controls).
Preprocess log(RPKM + 1) · low-expression & variance filtering · stratified 5-fold CV.
Features RF Gini importance · Boruta · biological curation against the IFN module.
Model Random Forest (500 trees) vs. limma signature, LASSO — RF wins on macro-F1.
Result AUROC 0.972 · macro-F1 0.95 · bal-acc 0.94.
Model 2
See how

Flare risk

"Will they be active at the next visit?"

Data GSE65391 (train) → GSE49454 (external) · longitudinal cohorts, SLEDAI ≥ 6.
Preprocess t → t+1 labelling · leakage-safe patient-level split · gene + clinical features.
Features Training-only probe selection · RF wrapper · clinical / treatment / temporal blocks.
Model RF, elastic net, GBM, weighted ensemble — gene + clinical RF wins externally.
Result AUC 0.823 · bal-acc 0.73 · accuracy 0.82 (external GSE49454).
Model 3
See how

Treatment response

"Will they respond to first-line therapy?"

Data GSE224705 · whole-blood microarray, lupus nephritis · SRI-4 endpoint.
Preprocess Probe filtering · collapse probes to gene symbols · balanced bootstrap on minority class.
Features RF Gini + Boruta + curation → 50-gene panel held out for evaluation.
Model limma, RF, LASSO, elastic net, linear SVM — Random Forest tops macro-F1.
Result AUROC 0.865 · macro-F1 0.79 · bal-acc 0.79.

Evaluation metrics

How we picked the winner.

One selection rule across every stage — accuracy plus the metrics that matter in a clinic.

01

Accuracy

Share of patients classified correctly on held-out cohorts.

Honest top-line, but hides class imbalance.

02

\(F_1\)-score

Harmonic mean of precision and recall on the minority class.

Penalises missed flares - the costly clinical error.

03

Generalisation

Patient-level CV plus a one-time external cohort (GSE49454).

Confirms the panel transfers across labs and platforms.

04

Inference speed

End-to-end latency from upload to risk band on a single patient.

Sub-second matters for a real bedside tool.

Selection rule Winner = best generalisation at clinic-grade speed, breaking ties by \(F_1\).

Results · external validation

Generalises to data it has never seen.

01 · Diagnosis · Random Forest
AUROC 0.972
Bal-Acc 0.939
Macro F1 0.950
Accuracy 0.974

0.97AUROC

Random Forest beat limma signature and LASSO on macro-F1. 5-fold stratified CV · GSE72509 · 117 samples.

02 · Flare risk · gene + clinical RF
AUC 0.823
Accuracy 0.821
Bal-Acc 0.734
Sensitivity 0.903

0.82AUC

External hold-out — never seen during training. Trained on GSE65391, tested on GSE49454.

03 · Treatment · Random Forest
AUROC 0.865
Accuracy 0.834
Macro F1 0.791
Bal-Acc 0.789

0.87AUROC

Random Forest top on macro-F1 across five candidates. 5-fold CV with balanced bootstrap · GSE224705 · SRI-4 endpoint.

Key finding Stable generalisation across cohorts, sub-second inference, and consistent gains over baselines on every stage's primary metric.

Final architecture

Random Forest, end-to-end.

01 · INPUT

Patient CSV

Whole-blood expression upload, validated against the gene panel.

02 · PREPROCESS

Clean & normalise

z-score against the reference distribution, missing-value imputation.

03 · FEATURES

Immune panel

~50 transcripts selected on the training cohort only.

04 · PREDICT

Random Forest

Three calibrated outputs - diagnosis, flare risk, treatment response.

05 · OUTPUT

Risk + drivers

Banded risk score with the top features behind every prediction.

No retraining at inference - the same trained model serves every patient request.

Live demonstration

From a blood sample to a recommendation in one click.

01Upload a patient expression CSV
02Auto-preprocess and score against the panel
03See diagnosis, flare and response risk with confidence
04Explore drivers & export a clinical summary

In summary.

The problem

Lupus care is reactive - molecular signal exists in blood but rarely reaches the clinic.

Our contribution

A three-step Random-Forest pipeline that classifies, predicts flares, and forecasts treatment response.

Why it matters

External validation holds up, inference is sub-second, and the console is usable by a non-ML clinician.

Where we go next.

Larger, prospective data

Validate on a contemporary, ancestry-diverse cohort to confirm calibration in the real world.

Multimodal learning

Layer autoantibody, cytokine and methylation features alongside the transcript module.

Cloud + explainability

Host as a clinician-facing web service with per-prediction SHAP-style attribution.

Thank you.

Biomed09 · Team
Manna Berry  ·  Kiwi Lin  ·  Udit Samant  ·  Hadi Shafat  ·  Minh Hieu Tran  ·  Jillian Zhao

DATA3888 · 2026
The University of Sydney

Appendix · 03 / 06

Dataset sources.

  • Hung, T., Pratt, G. A., Sundararaman, B., Townsend, M. J., Chaivorapol, C., Bhangale, T., Graham, R. R., Ortmann, W., Bhangale, T. R., Behrens, T. W., Yeo, G. W., & Chaussabel, D. (2015). The Ro60 autoantigen binds endogenous retroelements and regulates inflammatory gene expression in systemic lupus erythematosus [Data set; GSE72509]. NCBI Gene Expression Omnibus. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE72509
  • Banchereau, R., Hong, S., Cantarel, B., Baldwin, N., Baisch, J., Edens, M., Cepika, A.-M., Acs, P., Turner, J., Anguiano, E., Vinod, P., Kahn, S., Obermoser, G., Blankenship, D., Wakeland, E., Nassi, L., Gotte, A., Punaro, M., Liu, Y.-J., … Pascual, V. (2016). Personalized immunomonitoring uncovers molecular networks that stratify lupus patients. Cell, 165(3), 551–565. https://doi.org/10.1016/j.cell.2016.03.008
  • Chiche, L., Jourde-Chiche, N., Whalen, E., Presnell, S., Gersuk, V., Dang, K., Anguiano, E., Quinn, C., Burtey, S., Berland, Y., Kaplanski, G., Harlé, J.-R., Pascual, V., & Chaussabel, D. (2014). Modular transcriptional repertoire analyses of adults with systemic lupus erythematosus reveal distinct type I and type II interferon signatures. Arthritis & Rheumatology, 66(6), 1583–1595. https://doi.org/10.1002/art.38628
  • NCBI Gene Expression Omnibus. (2023). Whole-blood microarray expression in lupus nephritis: Treatment response by SRI-4 [Data set; GSE224705]. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE224705
Access · NCBI Gene Expression Omnibus (public)

Appendix · 04 / 06

Libraries & frameworks.

  • R Core Team. (2024). R: A language and environment for statistical computing (Version 4.x) [Computer software]. R Foundation for Statistical Computing. https://www.R-project.org/
  • Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R News, 2(3), 18–22.
  • Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939785
  • Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1–22. https://doi.org/10.18637/jss.v033.i01
  • Kursa, M. B., & Rudnicki, W. R. (2010). Feature selection with the Boruta package. Journal of Statistical Software, 36(11), 1–13. https://doi.org/10.18637/jss.v036.i11
  • Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research, 43(7), e47. https://doi.org/10.1093/nar/gkv007
  • Siriseriwan, W. (2019). smotefamily: A collection of oversampling techniques for class imbalance problem based on SMOTE (Version 1.3.1) [R package]. https://CRAN.R-project.org/package=smotefamily
  • Schloerke, B., & Allen, J. (2024). plumber: An API generator for R [R package]. https://www.rplumber.io/

Appendix · 05 / 06

Research papers & models.

Clinical background, methods, prior art, and scoring referenced in the deck.

  • National Institute of Arthritis and Musculoskeletal and Skin Diseases. (2022). Systemic lupus erythematosus (lupus) [Last reviewed October 2022]. Retrieved April 23, 2026, from https://www.niams.nih.gov/health-topics/lupus
  • MedlinePlus. (2024). Lupus [Last updated July 1, 2024]. Retrieved April 23, 2026, from https://medlineplus.gov/lupus.html
  • National Health Service. (2023). Lupus [Page last reviewed July 19, 2023]. Retrieved April 23, 2026, from https://www.nhs.uk/conditions/lupus/
  • MedlinePlus Medical Encyclopedia. (2025). Systemic lupus erythematosus [Review date January 28, 2025]. Retrieved April 23, 2026, from https://medlineplus.gov/ency/article/000435.htm
  • Aringer, M., & Bertsias, G. (2025). Early diagnosis of systemic lupus erythematosus. Rare Disease and Orphan Drugs Journal, 4, 13. https://doi.org/10.20517/rdodj.2024.59
  • Lin, A., Wakhlu, A., & Connelly, K. (2024). Disease activity assessment in systemic lupus erythematosus. Frontiers in Lupus, 2. https://doi.org/10.3389/flupu.2024.1442013
  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
  • Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939785
  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018
  • Kursa, M. B., & Rudnicki, W. R. (2010). Feature selection with the Boruta package. Journal of Statistical Software, 36(11), 1–13. https://doi.org/10.18637/jss.v036.i11
  • Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357. https://doi.org/10.1613/jair.953
  • Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems (Vol. 30, pp. 4765–4774). Curran Associates.
  • Gladman, D. D., Ibañez, D., & Urowitz, M. B. (2002). Systemic lupus erythematosus disease activity index 2000. The Journal of Rheumatology, 29(2), 288–291.
  • Furie, R., Petri, M. A., Wallace, D. J., Ginzler, E. M., Merrill, J. T., Stohl, W., Chatham, W. W., Strand, V., Weinstein, A., & Chevrier, M. (2009). Novel evidence-based systemic lupus erythematosus responder index. Arthritis & Rheumatism, 61(9), 1143–1151. https://doi.org/10.1002/art.24698

Appendix · 06 / 06

Team & acknowledgements.

DATA3888 · 2026
The University of Sydney

Team members Biomed09
Manna Berry email pending The University of Sydney, NSW 2006
Lezhi Lin llin0935@uni.sydney.edu.au School of Mathematics and Statistics F07, The University of Sydney, NSW 2006 Australia
Udit Samant email pending School of Computer Science J12, The University of Sydney, NSW 2006 Australia
Hadi Shafat hsha0153@uni.sydney.edu.au School of Computer Science J12, The University of Sydney, NSW 2006 Australia
Minh Hieu Tran email pending School of Computer Science J12, The University of Sydney, NSW 2006 Australia
Jillian Zhao yzha0369@uni.sydney.edu.au School of Computer Science J12, The University of Sydney, NSW 2006 Australia
Supervisors

Dr. Andy Tran · Elyna Lin

Many thanks to our supervisors Andy and Elyna for the weekly guidance, thoughtful feedback, and steady support throughout the project.

Acknowledgements
  • This slide is submitted in partial fulfillment of the assessment requirements for DATA3888 Data Science Capstone at The University of Sydney.
  • We also acknowledge the original data contributors and study participants behind the public GEO cohorts. Their shared expression and clinical metadata made the modelling, validation, and patient-level demonstrations possible.
  • Our work also rests on the work of open-source maintainers across R, Bioconductor, and the modelling libraries used here, as well as the DATA3888 teaching team for project structure, feedback, and course support.