Prediction of antimicrobial activity of peptides using relational machine learning

Andrea Szabóová, Ondřej Kuželka, F. Železný
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引用次数: 3

Abstract

We apply relational machine learning techniques to predict antimicrobial activity of peptides. We follow our successful strategy (Szabóová et al., MLSB 2010) to prediction of DNA-binding propensity of proteins from structural features. We exploit structure prediction methods to obtain peptides' spatial structures, then we construct the structural relational features. We use these relational features as attributes in a regression model. We apply this methodology to antimicrobial activity prediction of peptides achieving better predictive accuracies than a state-of-the-art approach.
利用关系机器学习预测多肽的抗菌活性
我们应用关系机器学习技术来预测肽的抗菌活性。我们遵循我们成功的策略(Szabóová等人,MLSB 2010),从结构特征预测蛋白质的dna结合倾向。利用结构预测方法获取多肽的空间结构,构建多肽的结构关系特征。我们在回归模型中使用这些关系特征作为属性。我们将这种方法应用于肽的抗菌活性预测,比最先进的方法具有更好的预测准确性。
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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