CAFA-evaluator: a Python tool for benchmarking ontological classification methods.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Bioinformatics advances Pub Date : 2024-03-14 eCollection Date: 2024-01-01 DOI:10.1093/bioadv/vbae043

Damiano Piovesan, Davide Zago, Parnal Joshi, M Clara De Paolis Kaluza, Mahta Mehdiabadi, Rashika Ramola, Alexander Miguel Monzon, Walter Reade, Iddo Friedberg, Predrag Radivojac, Silvio C E Tosatto

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Abstract

We present CAFA-evaluator, a powerful Python program designed to evaluate the performance of prediction methods on targets with hierarchical concept dependencies. It generalizes multi-label evaluation to modern ontologies where the prediction targets are drawn from a directed acyclic graph and achieves high efficiency by leveraging matrix computation and topological sorting. The program requirements include a small number of standard Python libraries, making CAFA-evaluator easy to maintain. The code replicates the Critical Assessment of protein Function Annotation (CAFA) benchmarking, which evaluates predictions of the consistent subgraphs in Gene Ontology. Owing to its reliability and accuracy, the organizers have selected CAFA-evaluator as the official CAFA evaluation software.

Availability and implementation: https://pypi.org/project/cafaeval.

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CAFA-evaluator：本体分类方法基准测试的 Python 工具。

我们介绍的 CAFA-evaluator 是一个功能强大的 Python 程序，旨在评估预测方法在具有分层概念依赖关系的目标上的性能。它将多标签评估推广到了现代本体，在现代本体中，预测目标来自有向无环图，并通过利用矩阵计算和拓扑排序实现了高效率。程序要求包括少量标准 Python 库，因此 CAFA-evaluator 易于维护。代码复制了蛋白质功能注释关键评估（CAFA）基准，该基准评估基因本体中一致子图的预测。由于其可靠性和准确性，主办方选择 CAFA-evaluator 作为 CAFA 的官方评估软件。可用性和实施：https://pypi.org/project/cafaeval。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Bioinformatics advances

CiteScore

1.60

自引率

0.00%

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