ProFAB

ProFAB provides a standardized benchmark and datasets to enable fair evaluation of machine-learning methods for protein functional annotation using Gene Ontology (GO) terms and Enzyme Commission (EC) numbers.

Key Features:

• Infrastructure for Fair Comparison: ProFAB provides a standardized infrastructure that facilitates fair comparison of machine-learning methods under predefined experimental settings.
• Reliable Datasets: ProFAB supplies filtered and preprocessed protein annotation datasets including curated positive and negative training and validation sets.
• Evaluation Options: ProFAB supports multiple training and evaluation configurations for function prediction methods.
• Integration with GO and EC: ProFAB enables predictions aligned to Gene Ontology (GO) terms and Enzyme Commission (EC) numbers for compatibility with established biological classifications.

Scientific Applications:

• Protein function prediction studies: ProFAB supports development and benchmarking of predictive models in computational and experimental protein function research.
• Drug discovery: ProFAB facilitates accurate functional annotation relevant to target identification and mechanism studies.
• Metabolic engineering and systems biology: ProFAB supports reconstruction and analysis that rely on enzyme annotations and GO-based functional assignments.

Methodology:

Curation and preprocessing of protein annotation data to produce reliable positive and negative training and validation sets, and support for applying machine-learning techniques with standardized datasets and predefined experimental settings for fair evaluation.