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Pattern analysis for the prediction of fungal pro-peptide cleavage sites

机译：模式分析预测真菌前肽裂解位点

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Support vector machines (SVMs) have many applications in investigating biological data from gene expression arrays to understanding EEG signals of sleep stages. In this paper, we have developed an application that will support the prediction of the pro-peptide cleavage site of fungal extracellular proteins which display mostly a monobasic or dibasic processing site. Many of the secretory proteins and peptides are synthesized as inactive precursors and they become active after post-translational processing. A collection of fungal proprotein sequences are used as a training data set. A specifically designed kernel is expressed as an application of the well-known Gaussian kernel via feature spaces defined for our problem. Rather than fixing the kernel parameters with cross validation or other methods, we introduce a novel approach that simultaneously performs model selection together with the test of accuracy and testing confidence levels. This leads us to higher accuracy at significantly reduced training times. The results of the server ProP1.0 which predicts pro-peptide cleavage sites are compared with the results of this study. A similar mathematical approach may be adapted to pro-peptide cleavage prediction in other eukaryotes.
机译：支持向量机（SVM）在研究来自基因表达阵列的生物学数据以了解睡眠阶段的EEG信号方面具有许多应用。在本文中，我们开发了一种应用程序，该应用程序将支持真菌细胞外蛋白的前肽切割位点的预测，这些肽主要显示一元或二元加工位点。许多分泌蛋白和肽被合成为无活性的前体，它们在翻译后加工后变得有活性。真菌原蛋白序列的集合用作训练数据集。经过专门设计的内核通过为我们的问题定义的特征空间表示为著名的高斯内核的应用。我们没有采用交叉验证或其他方法来固定内核参数，而是引入了一种新颖的方法，该方法可以同时执行模型选择以及准确性和置信度测试。这使我们在减少训练时间的同时提高了准确性。将预测前肽切割位点的服务器ProP1.0的结果与本研究的结果进行比较。类似的数学方法可以适用于其他真核生物中的前肽切割预测。

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