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Prediction of Post-Synaptic Activity in Proteins Using Recursive Feature Elimination

机译：递归特征消除预测蛋白质后突触后活性

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This work presents a new approach to predict post-synaptic activities in proteins. It uses a feature selection technique, called Recursive Feature Elimination, in order to select only the relevant features from the complete database. Once the reduced subset is found, Least Squares Support Vector Machine, a SVM based classifier, is used to predict its classes. The experiments were performed on a database that was harvested from Swiss Prot/Uniprot, a public domain database with a rich source of information for a very large number of proteins. The obtained results show that the proposed approach led to a reduced representation to the database, using only 6% of the original information, and yielded an improvement into the classification when compared to another two prediction techniques applied to the complete database, Decision Tree and Least Squares Support Vector Machine.
机译：这项工作提出了一种预测蛋白质后突触后活性的新方法。它使用特征选择技术，称为递归特征消除，以便仅从完整数据库中选择相关的功能。一旦找到了减少的子集，最小二乘支持向量机，基于SVM的分类器，用于预测其类。实验是在从瑞士Prot / Uniprot，一个公共领域数据库收获的数据库上进行的，具有丰富的信息来源，用于非常大量的蛋白质。所获得的结果表明，当与应用于完整数据库，决策树和最少的另外两种预测技术相比，所提出的方法使用仅6％的原始信息导致对数据库的表示减少，并产生了进入分类的改进正方形支持向量机。

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