Using supervised fuzzy clustering to predict protein structural classes
Using supervised fuzzy clustering to predict protein structural classes
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DOI：
10.1016/j.bbrc.2005.06.128
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发表时间：
2005-08-26
影响因子：
3.1
通讯作者：
Chou, KC
中科院分区：
文献类型：
--
作者：
Shen, HB;Yang, J;Chou, KC
蛋白质分类预测是蛋白质科学中一个重要而又诱人的课题。这不仅是因为由此获得的知识可以提供关于查询蛋白质的整体结构的有用信息，而且实践本身可以在技术上刺激可以直接应用于许多其他相关领域的新预测器的开发。在本文中，一种新的方法，所谓的“监督模糊聚类方法”的特点是利用类标签信息在训练过程中。基于这样的方法，一组“如果-那么”的模糊规则，用于预测蛋白质结构类提取的训练数据集。通过两个不同的工作数据集已经证明，由监督模糊聚类方法获得的总体成功预测率都高于由以前的研究者引入的无监督模糊c均值[C. T. Zhang，K.C. Chou，G.M.马焦拉。（1995）8，425435]。预计目前的预测器可能会发挥重要的补充作用，其他现有的预测器在这一领域，以进一步加强预测的能力，在蛋白质的结构类别和其他特征属性。(c)2005年爱思唯尔公司All rights reserved.
Prediction of protein classification is both an important and a tempting topic in protein science. This is because of not only that the knowledge thus obtained can provide useful information about the overall structure of a query protein, but also that the practice itself can technically stimulate the development of novel predictors that may be straightforwardly applied to many other relevant areas. In this paper, a novel approach, the so-called "supervised fuzzy clustering approach" is introduced that is featured by utilizing the class label information during the training process. Based on such an approach, a set of "if-then" fuzzy rules for predicting the protein structural classes are extracted from a training dataset. It has been demonstrated through two different working datasets that the overall success prediction rates obtained by the supervised fuzzy clustering approach are all higher than those by the unsupervised fuzzy c-means introduced by the previous investigators [C.T. Zhang, K.C. Chou, G.M. Maggiora. Protein Eng. (1995) 8, 425435]. It is anticipated that the current predictor may play an important complementary role to other existing predictors in this area to further strengthen the power in predicting the structural classes of proteins and their other characteristic attributes. (c) 2005 Elsevier Inc. All rights reserved.