Department of Health Informatics, Graduate School of Informatics, Middle East Technical University, Ankara, Turkey.
Department of Computer Engineering, Middle East Technical University, Ankara, Turkey.
Brief Bioinform. 2023 Mar 19;24(2). doi: 10.1093/bib/bbac627.
As the number of protein sequences increases in biological databases, computational methods are required to provide accurate functional annotation with high coverage. Although several machine learning methods have been proposed for this purpose, there are still two main issues: (i) construction of reliable positive and negative training and validation datasets, and (ii) fair evaluation of their performances based on predefined experimental settings. To address these issues, we have developed ProFAB: Open Protein Functional Annotation Benchmark, which is a platform providing an infrastructure for a fair comparison of protein function prediction methods. ProFAB provides filtered and preprocessed protein annotation datasets and enables the training and evaluation of function prediction methods via several options. We believe that ProFAB will be useful for both computational and experimental researchers by enabling the utilization of ready-to-use datasets and machine learning algorithms for protein function prediction based on Gene Ontology terms and Enzyme Commission numbers. ProFAB is available at https://github.com/kansil/ProFAB and https://profab.kansil.org.
随着生物数据库中蛋白质序列数量的增加，需要计算方法来提供具有高覆盖率的准确功能注释。尽管已经提出了几种机器学习方法用于此目的，但仍存在两个主要问题：(i)构建可靠的正、负训练和验证数据集，以及 (ii) 根据预定义的实验设置公平评估其性能。为了解决这些问题，我们开发了 ProFAB：开放蛋白质功能注释基准，这是一个为蛋白质功能预测方法的公平比较提供基础设施的平台。ProFAB 提供过滤和预处理的蛋白质注释数据集，并通过多种选项支持功能预测方法的训练和评估。我们相信，ProFAB 将通过基于基因本体论术语和酶委员会编号的利用现成的数据集和机器学习算法进行蛋白质功能预测，为计算和实验研究人员提供有用的工具。ProFAB 可在 https://github.com/kansil/ProFAB 和 https://profab.kansil.org 上获得。
