Our Predictive Models
Background
Cancer immunotherapy has revolutionized oncology, but predicting which patients will respond remains a significant challenge. Our models leverage tumor transcriptomic signatures to predict treatment response with unprecedented accuracy.
By analyzing gene expression patterns from thousands of tumor samples, we've developed machine learning models that can identify patients most likely to benefit from specific immunotherapy treatments, reducing unnecessary side effects and healthcare costs while improving outcomes.
K-Nearest Neighbors (KNN) Classification Model
How It Works
Our KNN model analyzes tumor gene expression profiles by comparing them to known patient outcomes. The algorithm identifies the K most similar historical cases and predicts treatment response based on their collective outcomes.
- →Processes high-dimensional transcriptomic data from tumor samples
- →Identifies similar patient profiles using distance metrics in gene expression space
- →Generates probabilistic predictions based on nearest neighbor outcomes
Head & Neck Cancer Immunotherapy Prediction
Upload patient transcriptomic data for real-time immunotherapy response prediction
Drop file here or click to upload
Supported formats: CSV, TXT, JSON
About This Model
This prediction tool uses a neural network trained on head and neck cancer patient data to predict immunotherapy response rates. The model analyzes gene expression patterns to identify biomarkers associated with treatment success, providing clinicians with data-driven insights for personalized treatment planning.
