| 哈斯矩阵图的G-蛋白偶联受体类型预测 |
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| 引用本文： | 肖绚,徐培杰.哈斯矩阵图的G-蛋白偶联受体类型预测[J].计算机工程,2011,37(18):204-205. |
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| 作者姓名： | 肖绚 徐培杰 |
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| 作者单位： | 景德镇陶瓷学院机械电子工程学院,江西景德镇,333403 |
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| 基金项目： | 国家自然科学基金资助项目，扛西省自然科学基金资助项目，教育部科学技术研究基金资助重点项目 |
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| 摘 要： | 利用氨基酸数字编码模型，将蛋白质序列转换为数字序列，根据偏序理论构建蛋白质哈斯矩阵。基于同一类型蛋白质哈斯矩阵图 具有相似图像纹理的假设，运用图像处理方法提取图像的几何矩作为伪氨基酸成分，对G-蛋白偶联受体类型分为2层进行预测，预测成功率分别为92.33%和85.48%。预测效果表明该方法是可行的。
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| 关 键 词： | 生物信息学 G-蛋白偶联受体 哈斯矩阵 模糊K近邻算法 Jackknife测试 |
| 收稿时间： | 2011-03-10 |
G-Protein Coupled Receptor Classes Prediction of Hasse Matrix Image |
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| XIAO Xuan,XU Pei-jie.G-Protein Coupled Receptor Classes Prediction of Hasse Matrix Image[J].Computer Engineering,2011,37(18):204-205. |
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| Authors: | XIAO Xuan XU Pei-jie |
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| Affiliation: | (School of Mechanical&Electronic Engineering,Jingdezhen Ceramic Institute,Jingdezhen 333403,China) |
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| Abstract: | Amino acid numeric coding model is used to convert protein sequences into numeric sequences,and the protein Hasse matrix is constructed based on partial ordering.It is assumed that proteins belonging to a same class must have some sort of similar texture of the protein Hasse matrix images.Based on this,geometric invariant moment factors derived from the image are used as the pseudo amino acid components to predict G-Protein Coupled Receptor（GPCR） classes in two levels.Through a benchmark dataset,the overall success rates achieved by the test are 92.33% and 85.48% in the first and second level respectively.Experimental results show that this method is viable. |
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| Keywords: | bioinformatics G-Protein Coupled Receptor（GPCR） Hasse matrix fuzzy K-nearest neighbor algorithm jackknife test |
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