Essential proteins possess critical functions for cell survival. Identifying essential proteins improves our understanding of how a cell works and also plays a vital role in the research fields of disease treatment and drug development. Recently, some machine-learning methods and ensemble learning methods have been proposed to identify essential proteins by introducing effective protein features. However, the ensemble learning method only used to focus on the choice of base classifiers. In this article, we propose a novel ensemble learning framework called multi-ensemble to integrate different base classifiers. The multi-ensemble method adopts the idea of multi-view learning and selects multiple base classifiers and trains those classifiers by continually adding the samples that are predicted correctly by the other base classifiers. We applied multi-ensemble to Yeast data and Escherichia coli data. The results show that our approach achieved better performance than both individual classifiers and the other ensemble learning methods.

中文翻译：

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一种用于识别必需蛋白质的新型多集合方法

必需蛋白质具有细胞存活的关键功能。识别必需蛋白质可以提高我们对细胞如何工作的理解，并且在疾病治疗和药物开发的研究领域也发挥着至关重要的作用。最近，一些机器学习方法和集成学习方法被提出来通过引入有效的蛋白质特征来识别必需的蛋白质。然而，集成学习方法仅用于关注基分类器的选择。在本文中，我们提出了一种新的集成学习框架，称为多集成来集成不同的基分类器。多集成方法采用多视图学习的思想，选择多个基分类器并通过不断添加其他基分类器正确预测的样本来训练这些分类器。大肠杆菌数据。结果表明，我们的方法比单个分类器和其他集成学习方法取得了更好的性能。