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Use of a Novel Grammatical Inference Approach in Classification of Amyloidogenic Hexapeptides

机译:新型语法推断方法在淀粉样生成六肽分类中的应用

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摘要

The present paper is a novel contribution to the field of bioinformatics by using grammatical inference in the analysis of data. We developed an algorithm for generating star-free regular expressions which turned out to be good recommendation tools, as they are characterized by a relatively high correlation coefficient between the observed and predicted binary classifications. The experiments have been performed for three datasets of amyloidogenic hexapeptides, and our results are compared with those obtained using the graph approaches, the current state-of-the-art methods in heuristic automata induction, and the support vector machine. The results showed the superior performance of the new grammatical inference algorithm on fixed-length amyloid datasets.
机译:通过在数据分析中使用语法推断,本文对生物信息学领域做出了新的贡献。我们开发了一种生成无星星正则表达式的算法,该算法被证明是很好的推荐工具,因为它们的特征在于观测到的和预测的二进制分类之间的相关系数相对较高。已经对三个淀粉样蛋白生成六肽数据集进行了实验,并将我们的结果与使用图法,启发式自动机诱导的最新技术以及支持向量机获得的结果进行了比较。结果表明,新的语法推理算法在固定长度的淀粉样数据集上具有优越的性能。

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