计算机工程 ›› 2011, Vol. 37 ›› Issue (18): 204-205.doi: 10.3969/j.issn.1000-3428.2011.18.068

• 人工智能及识别技术 • 上一篇    下一篇

哈斯矩阵图的G-蛋白偶联受体类型预测

肖 绚,徐培杰   

  1. (景德镇陶瓷学院机械电子工程学院,江西 景德镇 333403)
  • 收稿日期:2011-03-10 出版日期:2011-09-20 发布日期:2011-09-20
  • 作者简介:肖 绚(1970-),男,教授、博士,主研方向:智能信息处理,模式识别,生物信息学;徐培杰,硕士研究生
  • 基金项目:

    国家自然科学基金资助项目(60961003);江西省自然科学基金资助项目(2009GZS0064, 2010GZS0122);教育部科学技术研究基金资助重点项目(210116)

G-Protein Coupled Receptor Classes Prediction of Hasse Matrix Image

XIAO Xuan, XU Pei-jie   

  1. (School of Mechanical&Electronic Engineering, Jingdezhen Ceramic Institute, Jingdezhen 333403, China)
  • Received:2011-03-10 Online:2011-09-20 Published:2011-09-20

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。

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

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.

Key words: bioinformatics, G-Protein Coupled Receptor(GPCR), Hasse matrix, fuzzy K-nearest neighbor algorithm, jackknife test

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