Archives: “Machine Learning & AI”

Noninvasive Skin Protein Assessment to Guide Targeted Treatment Selection in Inflammatory Skin Diseases

Using DIPS, a non-invasive skin sampling technique, to build cytokine-based biomarker panels that predict biologic treatment response in psoriasis and atopic dermatitis.

Wearables and AI Framework for Inflammatory Arthritis Assessment and Management

Using wearable sensors and machine learning to analyze real-world movement and sleep data, this project aims to predict treatment response earlier and enable more personalized care for inflammatory arthritis.

Use of AI-Enhanced Pattern Recognition and Autoantibody Discovery to Improve Anti-Nuclear Antibody Testing

Modernizing anti-nuclear antibody testing by combining AI-enhanced image analysis with multiplex autoantibody profiling to improve diagnostic precision and uncover novel autoimmune biomarkers.

Biomarker Discovery for Early Prediction of Autoimmunity in Immunotherapy Patients Through Deep Immune Profiling and Temporal Graph Convolutional Networks

Using temporal graph machine learning and deep immune profiling to predict which cancer immunotherapy patients will develop autoimmune adverse events before they occur.

Targets of Autoimmune Attack in Acquired Aplastic Anemia

Identifying the autoantigens and T cell clonotypes driving immune attack in aplastic anemia to enable more precise diagnostics and targeted therapies.

In Vivo Target Identification and Machine Learning Validation of Autoimmune T Cell Tissue Residence

Identifying GSPT1 as an essential, targetable vulnerability of skin-resident memory T cells to enable durable remission in chronic inflammatory skin diseases without systemic immunosuppression.

High-Throughput Center for AutoImmune Therapeutic Discovery (HIT-AI)

Systematically identifying and validating repurposed FDA-approved drugs for over 160 autoimmune diseases using AI, knowledge graphs, and high-throughput experimental screening.

Machine learning approach to single cell transcriptomic analysis of Sjogren’s disease reveals altered activation states of B and T lymphocytes

ImmunoStruct enables multimodal deep learning for immunogenicity prediction

Agent-based modeling for personalized prediction of an experimental immune response to immunotherapeutic antibodies