GO bench

GO bench provides standardized benchmarking for machine-learning models that annotate proteins with Gene Ontology (GO) terms, enabling consistent evaluation of multi-label, multi-class protein function prediction.

Key Features:

• Standardized Benchmarking Dataset: A curated Gene Benchmarking database that serves as a reference dataset for evaluating protein-to-GO term annotation models.
• Preprocessing and Filtering: Preprocessing and filtering functionalities to select high-quality, relevant records for model training and evaluation.
• Configurable Presets: Options to define dataset subsets and filtering criteria via reusable presets for consistent experimental setups.
• Multi-label, Multi-class Support: Explicit benchmarking of the multi-label, multi-class nature of protein-to-GO term mapping.
• Model Evaluation and Leaderboards: Evaluation framework and leaderboards that compare trained models using standardized evaluation metrics.

Scientific Applications:

• Protein Function Characterization: Benchmarking machine-learning models for annotating protein functions with Gene Ontology terms.
• Comparative Model Assessment: Enabling comparison of different gene annotation models on a common dataset and metrics.
• Improving Annotation Accuracy: Supporting research aimed at improving the accuracy and reliability of gene annotations to advance understanding of molecular biological processes.

Methodology:

Preprocessing and filtering of data to retain high-quality, relevant records; application of custom presets to define dataset subsets; evaluation of trained models on leaderboards using standardized metrics.