LAFA: A Framework for Reproducible L ongitudinal A ssessment of Protein F unction A nnotation Models

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ID: 324423
2026
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Abstract
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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openalex_W7201996962 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors An Phan, Yanli Wang, Frimpong Boadu, Jianlin Cheng, Predrag Radivojac, Iddo Friedberg
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Journal Bioinformatics advances
Year 2026
DOI
10.1093/bioadv/vbag221
URL
https://doi.org/10.1093/bioadv/vbag221
Keywords Keywords not found

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