Protein Structural Class Determination Using Support Vector Machines

机译：使用支持向量机的蛋白质结构类别确定

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Proteins can be classified into four structural classes (all-α, all-β, α/β, α+β) according to their secondary structure composition. In this paper, we predict the structural class of a protein from its Amino Acid Composition (AAC) using Support Vector Machines (SVM). A protein can be represented by a 20 dimensional vector according to its AAC. In addition to the AAC, we have used another feature set, called the Trio Amino Acid Composition (Trio AAC) which takes into account the amino acid neighborhood information. We have tried both of these features, the AAC and the Trio AAC, in each case using a SVM as the classification tool, in predicting the structural class of a protein. According to the Jackknife test results, Trio AAC feature set shows better classification performance than the AAC feature.

机译：根据蛋白质的二级结构组成，可以将其分为四个结构类别（全α，全β，α/β，α+β）。在本文中，我们使用支持向量机（SVM）从其氨基酸成分（AAC）预测蛋白质的结构类别。蛋白质可以根据其AAC用20维向量表示。除了AAC，我们还使用了另一个功能集，称为三重氨基酸组成（Trio AAC），它考虑了氨基酸邻域信息。我们分别使用SVM作为分类工具尝试了AAC和Trio AAC这两个功能，以预测蛋白质的结构类别。根据折刀测试结果，Trio AAC功能集显示出比AAC功能更好的分类性能。

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《International Symposium on Computer and Information Sciences(ISCIS 2004); 20041027-29; Kemer-Antalya(TR)》|2004年|P.82-89|共8页
会议地点 Kemer-Antalya(TR)
作者
Zerrin Isik; Benin Yanikoglu; Ugur Sezerman;
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作者单位

Sabanci University, 34956, Tuzla, Istanbul, Turkey;

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正文语种 eng
中图分类计算技术、计算机技术;
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1. An ensemble classifier of support vector machines used to predict protein structural classes by fusing auto covariance and pseudo-amino acid composition. [J] . Wu J, Li ML, Yu L The protein journal . 2010,第1期

机译：支持向量机的整体分类器，用于通过融合自协方差和伪氨基酸组成来预测蛋白质结构分类。
2. An Ensemble Classifier of Support Vector Machines Used to Predict Protein Structural Classes by Fusing Auto Covariance and Pseudo-Amino Acid Composition [J] . Jiang Wu, Meng-Long Li, Le-Zheng Yu, The Protein Journal . 2010,第1期

机译：支持向量机的集成分类器，用于通过融合自协方差和伪氨基酸组成来预测蛋白质结构分类
3. Granular support vector machine to identify unknown structural classes of protein [J] . Hassan Rohayanti, Othman Razib M., Shah Zuraini A. International journal of data mining and bioinformatics . 2015,第4期

机译：颗粒支持向量机识别未知的蛋白质结构类别
4. Protein Structural Class Determination Using Support Vector Machines [C] . Zerrin Isik, Benin Yanikoglu, Ugur Sezerman International Symposium on Computer and Information Sciences . 2004

机译：使用支持向量机的蛋白质结构类测定
5. Prediction of catalytic site in proteins using support vector machine classifier and conservation of prediction approach. [D] . Petrova, Natalia V. 2008

机译：使用支持向量机分类器预测蛋白质中的催化位点并保留预测方法。
6. Support Vector Machines for predicting protein structural class [O] . Yu-Dong Cai, Xiao-Jun Liu, Xue-biao Xu, 2001

机译：支持向量机用于预测蛋白质结构分类
7. Protein structural class determination using support vector machines [O] . Işık, Zerrin, Isik, Zerrin, Yanıkoğlu, Berrin, 2004

机译：使用支持向量机确定蛋白质结构类别
8. Support Vector Machines with Selective Kernel Scaling for Protein Classification and Identification of Key Amino Acid Positions. [R] . Zavaljevski, N., Stevens, F. J., Reifman, J. 2002

机译：具有选择性核标度的支持向量机用于蛋白质分类和关键氨基酸位置的鉴定。

Protein Structural Class Determination Using Support Vector Machines

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