Comparisons of Different Feature Sets for Predicting Carbohydrate-Binding Proteins From Amino Acid Sequences Using Support Vector Machine

机译：用载体载体机预测碳水化合物结合蛋白的不同特征集的比较

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Proteins which can interact with sugar chains but do not modify them are called as Carbohydrate-binding proteins. These proteins have several biological importance. To predict them computationally with SVM classifier, we have developed different feature sets-based on secondary structures and selective physicochemical properties of the constituent amino acids. The feature set formed with combination of both the secondary structures and physicochemical properties gives better prediction accuracy (up to 89.19 %). We have also prepared an up-to-date dataset of carbohydrate-binding proteins and non-carbohydrate-binding proteins in this work.

机译：可以与糖链相互作用但不改性它们的蛋白质称为碳水化合物结合蛋白。这些蛋白质有几种生物重要性。为了用SVM分类器计算它们，我们已经开发了不同的特征集 - 基于次要结构和组成氨基酸的选择性物理化学性质。具有二次结构和物理化学特性的组合形成的特征集可以提供更好的预测精度（高达89.19％）。我们还在本工作中制备了碳水化合物结合蛋白和非碳水化合物结合蛋白的最新数据集。

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《International Conference on Bio-inspired Computing, Theories and Applications》|2013年||共11页
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Suchandra Payal; Piyali Chatterjee; Subhadip Basu; Mahantapas Kundu; Mita Nasipuri;
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中图分类 TP18-532;
关键词
Carbohydrate-binding proteins; Feature sets; Secondary structures; Selective physicochemical properties;

机译：碳水化合物结合蛋白;特征套;二次结构;选择性物理化学性质;

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1. Support vector machines for predicting rRNA-, RNA-, and DNA-binding proteins from amino acid sequence. [J] . Cai Y, Lin SL Biochimica et biophysica acta: international journal of biochemistry and biophysics . 2003,第1a2期

机译：支持向量机，可从氨基酸序列预测rRNA，RNA和DNA结合蛋白。
2. An ensemble of support vector machines for predicting the membrane protein type directly from the amino acid sequence [J] . Loris Nanni, Alessandra Lumini Amino acids . 2008,第3期

机译：支持向量机的集合，可直接从氨基酸序列预测膜蛋白类型
3. An ensemble of support vector machines for predicting the membrane protein type directly from the amino acid sequence [J] . Loris Nanni, Alessandra Lumini Amino Acids . 2008,第3期

机译：一整套支持向量机，可直接从氨基酸序列预测膜蛋白类型
4. Comparisons of Different Feature Sets for Predicting Carbohydrate-Binding Proteins From Amino Acid Sequences Using Support Vector Machine [C] . Suchandra Payal, Piyali Chatterjee, Subhadip Basu, International Conference on Bio-inspired Computing, Theories and Applications . 2013

机译：用载体载体机预测碳水化合物结合蛋白的不同特征集的比较
5. Evolutionary Analysis of the CAP Superfamily of Proteins using Amino Acid Sequences and Splice Sites [D] . Abraham, Anup 2016

机译：使用氨基酸序列和剪接位点的蛋白质CAP超家族的进化分析。
6. Prediction of Carbohydrate-Binding Proteins from Sequences Using Support Vector Machines [O] . Seizi Someya, Masanori Kakuta, Mizuki Morita, 2010

机译：使用支持向量机从序列预测糖结合蛋白
7. Support Vector Machines for Predicting Protein Structural Classes via Pseudo Images Derived From Amino Acid Sequences [O] . Betul Akcesme, Mehmet Can 2016

机译：通过衍生自氨基酸序列的伪图像来支持向量机器预测蛋白质结构类
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。

Comparisons of Different Feature Sets for Predicting Carbohydrate-Binding Proteins From Amino Acid Sequences Using Support Vector Machine

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