FORESEE

FORESEE standardizes benchmarking of translational drug response models using omics data to predict efficacy of anti-cancer compounds.

Key Features:

• Uniform data format: Provides a consistent data format for integrating public cell line and patient datasets.
• Standardized environment: Implements state-of-the-art data pre-processing methods, model training algorithms, and validation techniques for drug response prediction pipelines.
• Modular implementation: Offers a modular architecture that enables development of combinatorial models and extension of pipeline components.
• R-package implementation: Distributed as an R package for computational reproducibility and pipeline execution.

Scientific Applications:

• Benchmarking computational approaches: Provides a standardized framework to benchmark new computational methods against established ones in translational drug response modeling.
• Model development and improvement: Enables development and refinement of predictive models for translating in vitro findings to clinical predictions for anti-cancer compounds.

Methodology:

Integrates data pre-processing, model training, and validation techniques within a modular architecture that allows customization and extension of pipeline components.