Training host-pathogen protein-protein interaction predictors
Training host-pathogen protein-protein interaction predictors
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DOI：
10.1142/s0219720018500142
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发表时间：
2018-08-01
影响因子：
1
通讯作者：
Minhas, Fayyaz Ul Amir Afsar
中科院分区：
文献类型：
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作者：
Basit, Abdul Hannan;Abbasi, Wajid Arshad;Minhas, Fayyaz Ul Amir Afsar
蛋白-蛋白相互作用（PPIs）的检测在分子生物学中起着至关重要的作用。特别是病原性感染是由宿主和病原体蛋白相互作用引起的。传染病。传统的湿实验室PPI检测技术在成本和大规模应用方面存在局限性。因此，计算方法被开发来预测ppi。本研究旨在开发机器学习模型来预测物种间ppi，并对hpi特别感兴趣。具体来说，我们专注于寻找在开发HPI预测器时出现的三个问题的答案：(1)如何选择负训练样例？(2)基于与正例的相似度为单个负例分配样本权重是否能提高泛化性能？(3)在训练和评估过程中，与正样本相比，负样本的大小应该是多少？我们比较了两种可用的负抽样方法：随机抽样和DeNovo抽样，我们的实验表明，DeNovo抽样具有更好的准确性。然而，我们的实验也表明，通过使用软DeNovo方法可以进一步提高泛化性能，该方法在训练过程中将样本权重分配给负例，与已知正例的相似度成反比。基于我们的发现，我们还开发了一种名为HOPITOR（宿主-病原体相互作用预测器）的HPI预测器，可以预测人类和病毒蛋白之间的相互作用。HOPITOR web服务器的访问地址为http://faculty.pieas.edu.pk/fayyaz/software.html#HoPItor。
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.