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首页> 外文期刊>International journal of open source software & processes >Open-Source Essential Protein Prediction Model by Integrating Chi-Square and Support Vector Machine
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Open-Source Essential Protein Prediction Model by Integrating Chi-Square and Support Vector Machine

机译：通过整合Chi-Square和支持向量机的开源必需蛋白预测模型

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摘要

Identification and analysis of protein play a vital role in drug design and disease prediction. There are several open-source applications that have been developed for identifying essential proteins which are based on biological or topological features. These techniques infer the possibility of proteins to be essential by using the network topology and feature selection, which can ignore some of the features to reduce the complexity and, subsequently, results in less accuracy. In the paper, the authors have used selenium driver to scrap the dataset. Later, the authors integrated the chi-square method with support vector machine for the prediction of essential proteins in baker yeast. Here, chi-square is a test of dissimilarity used for altering the record, and afterward, the support vector machine is used to classify the test dataset. The results show that the proposed model Chi-SVM model achieves an accuracy of 99.56%, whereas BC and CC achieved an accuracy of 84.0% and 86.0%. Finally, the proposed model is validated using Statistical performance measures such as PPA, NPA, SA, and STA.
机译：蛋白质的鉴定和分析在药物设计和疾病预测中发挥着至关重要的作用。已经开发了几种开源应用，用于识别基于生物或拓扑特征的基本蛋白质。这些技术推断出通过使用网络拓扑和特征选择来实现蛋白质的可能性，这可以忽略一些特征来降低复杂性，并且随后导致较低的准确性。在本文中，作者使用Selenium Driver来废除数据集。后来，作者将Chi-Square方法与支持向量机集成，用于预测面包酵母中必需蛋白质。这里，Chi-Square是用于改变记录的异化性的测试，然后，支持向量机用于对测试数据集进行分类。结果表明，拟议的CHI-SVM模型的精度为99.56％，而BC和CC达到了84.0％和86.0％的准确度。最后，使用统计性能措施（如PPA，NPA，SA和STA）验证所提出的模型。

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