• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International conference on future information technology;ICFIT 2010 >Nuclear Receptor Sequence Visualization and Subfamily Classification
【24h】

Nuclear Receptor Sequence Visualization and Subfamily Classification

机译：核受体序列可视化和亚科分类

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Nuclear receptors (NRs) are one of the most abundant classes of transcriptional regulators in animals (metazoans). However, it is both time-consuming and costly to determining their structural and functional information. In this study, we present a novel method to visualizing the NR sequences and a predictor called "NRSP-CA" to predict the functional subfamily type, where "CA" stands for "Cellular Automaton", meaning that the CA images (CAI) have been utilized to reveal the pattern features hidden in piles of long and complicated protein sequences. Meanwhile, the geometric moments extracted from the CAI are used to represent the samples of proteins through their pseudo amino acid composition. The classification was achieved on the basis of Fuzzy K nearest neighbor (FKNN) classifier. The overall predictive accuracy about 72% and 91% have been achieved through the rigorous jackknife cross-validation and independent test on a nuclear receptor benchmark dataset with low redundancy derived from the NucleaRDB. This indicates that our method can be a useful associated tool for subfamily recognition of NRs.
机译：核受体（NRs）是动物（metazoans）中最丰富的转录调节因子之一。但是，确定其结构和功能信息既费时又费钱。在这项研究中，我们提出了一种可视化NR序列的新方法，并使用一种称为“ NRSP-CA”的预测子来预测功能亚科类型，其中“ CA”代表“细胞自动机”，这意味着CA图像（CAI）具有被用来揭示隐藏在长而复杂的蛋白质序列中的模式特征。同时，从CAI中提取的几何矩用于通过蛋白质的假氨基酸组成表示蛋白质样品。该分类是基于模糊K最近邻（FKNN）分类器实现的。通过严格的折刀交叉验证和对来自NucleaRDB的低冗余度的核受体基准数据集的严格测试，已经实现了约72％和91％的总体预测准确性。这表明我们的方法对于亚家族的NRs识别可能是有用的相关工具。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号