LAFA: A Framework for Reproducible Longitudinal Assessment of Protein Function Annotation Models
An Phan, Yanli Wang, Frimpong Boadu, Maxat Kulmanov, Robert Hoehndorf + 3 more
TLDR
LAFA is a new framework for continuous, reproducible, and longitudinal assessment of protein function prediction models, addressing CAFA's periodic limitation.
Key contributions
- Introduces LAFA, a persistent system for continuous benchmarking of protein function prediction.
- Enables up-to-date, robust comparative assessment of methods against evolving ground truth data.
- Supports containerized prediction models, accelerating iteration and ensuring reproducibility.
- Provides a dynamic, fine-grained view of progress in protein function annotation over time.
Why it matters
The existing CAFA initiative provides only periodic evaluations, leaving a gap for continuous assessment as protein function annotations evolve. LAFA addresses this by offering a persistent, dynamic benchmarking platform. This ensures methods are consistently evaluated against current data, accelerating development and improving reliability in computational biology.
Original Abstract
Motivation: Protein function prediction is a challenging task and an open problem in computational biology. The Critical Assessment of protein Function Annotation (CAFA) is a triennial, community-driven initiative that provides an independent, large-scale evaluation of computational methods for protein function prediction through time-delayed benchmarking experiments. CAFA has played a key role in highlighting high-performing methodologies and fostering detailed analysis and exchange of ideas. However, outside the periodic CAFA challenges, there is no platform for the continuous evaluation of newly developed methods and tracking performance as function annotations accumulate. Results: Here we introduce the Longitudinal Assessment of Protein Function Annotation Models server (LAFA) as a persistent benchmarking system for protein function prediction methods. LAFA provides a continuous evaluation of containerized function prediction methods, enabling up-to-date and robust comparative assessment of method performance under evolving ground truth. LAFA accelerates methodological iteration, supports reproducibility, and offers a more dynamic and fine-grained view of progress in protein function prediction. Code and Data Availability: LAFA is available at https://functionbench.net/. Detailed evaluation results can be found at https://github.com/anphan0828/CAFA_forever
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