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Training host-pathogen protein-protein interaction predictors

机译：培训宿主病原体蛋白质 - 蛋白质相互作用预测器

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摘要

Detection of protein-protein interactions (PPIs) plays a vital role in molecular biology. Particularly, pathogenic infections are caused by interactions of host and pathogen proteins. infectious diseases. Conventional wet lab PPI detection techniques have limitations in terms of cost and large-scale application. Hence, computational approaches are developed to predict PPIs. This study aims to develop machine learning models to predict inter-species PPIs with a special interest in HPIs. Specifically, we focus on seeking answers to three questions that arise while developing an HPI predictor: (1) How should negative training examples be selected? (2) Does assigning sample weights to individual negative examples based on their similarity to positive examples improve generalization performance? and, (3) What should be the size of negative samples as compared to the positive samples during training and evaluation? We compare two available methods for negative sampling: random versus DeNovo sampling and our experiments show that DeNovo sampling offers better accuracy. However, our experiments also show that generalization performance can be improved further by using a soft DeNovo approach that assigns sample weights to negative examples inversely proportional to their similarity to known positive examples during training. Based on our findings, we have also developed an HPI predictor called HOPITOR (Host-Pathogen Interaction Predictor) that can predict interactions between human and viral proteins. The HOPITOR web server can be accessed at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#HoPItor.
机译：检测蛋白质 - 蛋白质相互作用（PPI）在分子生物学中起着至关重要的作用。特别地，致病感染是由宿主和病原体蛋白的相互作用引起的。传染性疾病。传统的湿实验室PPI检测技术在成本和大规模应用方面具有局限性。因此，开发了计算方法以预测PPI。本研究旨在开发机器学习模型，以预测物种间PPI具有特别兴趣的HPI。具体而言，我们专注于寻求在开发HPI预测指标时出现的三个问题的答案：（1）如何选择负培训例子？ （2）基于它们与正示例的相似性改善泛化性能，将样本权重分配给各个否定例子？ （3）与训练和评估期间的阳性样本相比，应该是阴性样本的大小应该是什么？我们比较两种可用的消极采样方法：随机与Denovo采样和我们的实验表明Denovo采样提供更好的准确性。然而，我们的实验还表明，通过使用软Denovo方法可以进一步提高泛化性能，该方法可以将样本权重分配给训练期间与已知正示例的相似性成反比地成反比。基于我们的研究结果，我们还开发了一种称为Hopitor（宿主病原体相互作用预测因子）的HPI预测因子，其可以预测人与病毒蛋白之间的相互作用。 Hopitor Web服务器可以在URL访问：http：//faculty.pieas.edu.pk/fayyaz/software.html#hopitor。

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