machine-learning-for-omics

What problem does it solve?

Supervised learning on omics-derived features to enable biomarker discovery, risk prediction, and interpretable insights, all within a coherent workflow.

Core Features & Use Cases

• End-to-end workflow for predictive modeling, biomarker discovery, survival analysis, and model explainability on omics features.
• Define data splits, calibration checks, and robust evaluation to ensure trustworthy results.
• Use Case: classify disease status, predict patient survival, or identify potential biomarkers from omics datasets.

Quick Start

Train and evaluate a model on your omics feature matrix to obtain a baseline predictor and interpretation summaries.