Amyloid is generally an aggregate of insoluble fibrin; its abnormal deposition is the pathogenic mechanism of various diseases, such as Alzheimer’s disease and type II diabetes. Therefore, accurately identifying amyloid is necessary to understand its role in pathology. We proposed a machine learning-based prediction model called PredAmyl-MLP, which consists of the following three steps: feature extraction, feature selection, and classification. In the step of feature extraction, seven feature extraction algorithms and different combinations of them are investigated, and the combination of SVMProt-188D and tripeptide composition (TPC) is selected according to the experimental results. In the step of feature selection, maximum relevant maximum distance (MRMD) and binomial distribution (BD) are, respectively, used to remove the redundant or noise features, and the appropriate features are selected according to the experimental results. In the step of classification, we employed multilayer perceptron (MLP) to train the prediction model. The 10-fold cross-validation results show that the overall accuracy of PredAmyl-MLP reached 91.59%, and the performance was better than the existing methods.

中文翻译：

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PredAmyl-MLP：使用多层感知器预测淀粉样蛋白

淀粉样蛋白通常是不溶性纤维蛋白的聚集体；它的异常沉积是各种疾病的致病机制，如阿尔茨海默病和II型糖尿病。因此，准确识别淀粉样蛋白对于了解其在病理学中的作用是必要的。我们提出了一种基于机器学习的预测模型，称为 PredAmyl-MLP，它由以下三个步骤组成：特征提取、特征选择和分类。在特征提取步骤中，研究了7种特征提取算法及其不同组合，根据实验结果选择了SVMProt-188D与三肽组合物（TPC）的组合。在特征选择步骤中，最大相关最大距离（MRMD）和二项分布（BD）分别为 用于去除冗余或噪声特征，并根据实验结果选择合适的特征。在分类步骤中，我们采用多层感知器（MLP）来训练预测模型。10折交叉验证结果表明，PredAmyl-MLP的整体准确率达到了91.59%，性能优于现有方法。