Feature selection and the class imbalance problem in predicting protein function from sequence.
Feature selection and the class imbalance problem in predicting protein function from sequence.
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DOI：
10.2165/00822942-200594030-00004
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发表时间：
2005-01-01
期刊：
影响因子：
--
通讯作者：
Gilbert, David
中科院分区：
文献类型：
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作者：
Al-Shahib, Ali;Breitling, Rainer;Gilbert, David
当通过序列同源性预测蛋白质功能的标准方法失败时，可以使用只需要氨基酸序列来预测功能的其他替代方法。其中一种方法是使用机器学习直接根据氨基酸序列特征预测蛋白质功能。然而，在成功的功能预测之前，有两个问题需要考虑：识别歧视性特征，以及克服训练数据中大量不平衡的挑战。结果表明，与标准的机器学习方法相比，通过应用特征子集选择，然后对多数类进行欠采样，生成的支持向量机(SVM)分类器明显更好。除了揭示所选择的特征有可能促进我们对序列和功能之间的关系的理解之外，我们还表明，产生完全平衡的数据的欠采样显著提高了性能。支持向量机与特征选择和完全欠采样相结合的方法获得了最好的识别能力，该方法的性能明显优于其他竞争学习算法。我们的结论是，这种组合方法可以生成功能强大的机器学习分类器，用于直接从序列预测蛋白质功能。
When the standard approach to predict protein function by sequence homology fails, other alternative methods can be used that require only the amino acid sequence for predicting function. One such approach uses machine learning to predict protein function directly from amino acid sequence features. However, there are two issues to consider before successful functional prediction can take place: identifying discriminatory features, and overcoming the challenge of a large imbalance in the training data. We show that by applying feature subset selection followed by undersampling of the majority class, significantly better support vector machine (SVM) classifiers are generated compared with standard machine learning approaches. As well as revealing that the features selected could have the potential to advance our understanding of the relationship between sequence and function, we also show that undersampling to produce fully balanced data significantly improves performance. The best discriminating ability is achieved using SVMs together with feature selection and full undersampling; this approach strongly outperforms other competitive learning algorithms. We conclude that this combined approach can generate powerful machine learning classifiers for predicting protein function directly from sequence.