This project develops a machine learning model to classify individuals as healthy, having rheumatoid arthritis (RA), or systemic lupus erythematosus (SLE) using RNA-Seq gene expression data. The project also identifies significant genes as potential biomarkers, leveraging SGDClassifier and XGBoost models.

Repository for Multi Omics data Integration for Transcriptomics and Metabolomics in RA mouse models

Two staged approach to predict joint level damage from hand and feet radiographs

Cast different EHR (electronic health record) layers to a shared latent space to identify patient subtypes

R code for the data managment and statistical analysis performed for the paper Associations Between Rheumatoid Arthritis, Incident Heart Failure and Left Ventricular Ejection Fraction

Repository for supplementary data

Machine learning pipeline to enhance patient triaging by predicting diagnosis based on referral letters

Code for Jonsson*, Zhang*, et al, TteK (Granzyme K+ CD8 T cells), Science Translational Medicine, 2022: the core population of inflamed human tissue-associated CD8 T cells.