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A novel synonymous processing method based on amino acid substitution matrics for the classification of G-protein-coupled receptors

机译：基于氨基酸取代基质的G蛋白偶联受体分类的新型同义加工方法

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摘要

Extracting valuable features and filtering out redundancy are the key challenges to determine the overall classification performance for G-protein-coupled receptors (GPCRs). In this study, we consider improving the feature synonym problem, and put forward a novel feature knowledge mining strategy based on functional word clustering and integration. The essence behind the method is the novel feature knowledge mining strategy. Through evaluating the independence of each candidate feature using the evolutionary hypothesis based on residue substitution matrices, clustering candidate features, and fusing them by retaining the main functional words, the proposed strategy adds a layer between the feature extraction layer and the prediction layer. Based on the proposed method, four classic machine learning algorithms in conjunction with the feature extraction method were applied to classify GPCRs at all family levels. Surprisingly, these classifiers achieve considerable performance in almost all evaluation criteria which indicated the validity and superiority of the proposed molecular evolution based feature extraction method.
机译：提取有价值的特征和过滤冗余是确定G蛋白偶联受体（GPCR）的整体分类性能的关键挑战。在这项研究中，我们考虑提高功能同义词问题，并提出了一种基于功能词聚类和集成的新颖特征知识挖掘策略。该方法背后的本质是新颖的特色知识挖掘策略。通过基于残留替代矩阵，聚类候选特征的进化假设来评估每个候选特征的独立性，并通过保留主要功能词来融合它们，所提出的策略在特征提取层和预测层之间添加层。基于所提出的方法，应用了四种经典机器学习算法与特征提取方法一起应用于对所有家庭级别进行分类GPCR。令人惊讶的是，这些分类器几乎可以在几乎所有评估标准中实现了相当大的性能，这表明了所提出的基于分子进化的特征提取方法的有效性和优越性。

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