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Domain-Domain Interaction Identification with a Feature Selection Approach

机译：特征选择方法的域-域交互识别

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摘要

The protein-protein interactions (PPIs) are generally assumed to be mediated by domain-domain interactions (DDIs). Many computational methods have been proposed based on this assumption to predict DDIs from available data of PPIs. However, most of the existing methods are generative methods that consider only PPI data without taking into account non-PPIs. In this paper, we propose a novel discriminative method for predicting DDIs from both PPIs and non-PPIs, which improves the prediction reliability. In particular, the DDI identification is formalized as a feature selection problem, which is equivalent to the parsimonious principle and is able to predict both DDIs and PPIs in a systematic and accurate manner. The numerical results on benchmark dataset demonstrate that formulating DDI prediction as a feature selection problem can predict DDIs from PPIs in a reliable way, which in turn is able to verify and further predict PPIs based on inferred DDIs.
机译：通常假定蛋白质-蛋白质相互作用（PPI）由域-域相互作用（DDI）介导。基于此假设，已经提出了许多计算方法，以根据PPI的可用数据预测DDI。但是，大多数现有方法是仅考虑PPI数据而不考虑非PPI的生成方法。在本文中，我们提出了一种新的判别方法，用于从PPI和非PPI预测DDI，从而提高了预测的可靠性。特别是，将DDI识别形式化为一个特征选择问题，这等效于简约原则，并且能够以系统且准确的方式预测DDI和PPI。基准数据集上的数值结果表明，将DDI预测公式化为特征选择问题可以可靠地从PPI预测DDI，从而可以基于推断的DDI验证和进一步预测PPI。

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