Mansoori Eghbal G, Zolghadri Mansoor J, Katebi Seraj D
Department of Computer Science and Engineering, School of Engineering, Shiraz University, Shiraz, Iran.
IEEE Trans Nanobioscience. 2009 Mar;8(1):92-9. doi: 10.1109/TNB.2009.2016484. Epub 2009 Mar 21.
In this paper, we have proposed a fuzzy rule-based classifier for assigning amino acid sequences into different superfamilies of proteins. While the most popular methods for protein classification rely on sequence alignment, our approach is alignment-free and so more human readable. It accounts for the distribution of contiguous patterns of n amino acids ( n-grams) in the sequences as features, alike other alignment-independent methods. Our approach, first extracts a plenty of features from a set of training sequences, then selects only some best of them, using a proposed feature ranking method. Thereafter, using these features, a novel steady-state genetic algorithm for extracting fuzzy classification rules from data is used to generate a compact set of interpretable fuzzy rules. The generated rules are simple and human understandable. So, the biologists can utilize them, for classification purposes, or incorporate their expertise to interpret or even modify them. To evaluate the performance of our fuzzy rule-based classifier, we have compared it with the conventional nonfuzzy C4.5 algorithm, beside some other fuzzy classifiers. This comparative study is conducted through classifying the protein sequences of five superfamily classes, downloaded from a public domain database. The obtained results show that the generated fuzzy rules are more interpretable, with acceptable improvement in the classification accuracy.
在本文中，我们提出了一种基于模糊规则的分类器，用于将氨基酸序列分配到不同的蛋白质超家族中。虽然最流行的蛋白质分类方法依赖于序列比对，但我们的方法无需比对，因此更易于理解。与其他不依赖比对的方法一样，它将序列中n个氨基酸的连续模式（n元组）的分布作为特征。我们的方法首先从一组训练序列中提取大量特征，然后使用一种提出的特征排序方法仅选择其中一些最佳特征。此后，利用这些特征，一种用于从数据中提取模糊分类规则的新型稳态遗传算法被用于生成一组紧凑的可解释模糊规则。生成的规则简单且易于理解。因此，生物学家可以将它们用于分类目的，或者结合他们的专业知识来解释甚至修改它们。为了评估我们基于模糊规则的分类器的性能，除了一些其他模糊分类器外，我们还将其与传统的非模糊C4.5算法进行了比较。这项比较研究是通过对从公共领域数据库下载的五个超家族类别的蛋白质序列进行分类来进行的。所得结果表明，生成的模糊规则更易于解释，并且在分类准确率上有可接受的提高。
