• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>PLoS Computational Biology >Probabilistic Interaction Network of Evidence Algorithm and its Application to Complete Labeling of Peak Lists from Protein NMR Spectroscopy
【2h】

Probabilistic Interaction Network of Evidence Algorithm and its Application to Complete Labeling of Peak Lists from Protein NMR Spectroscopy

机译：证据算法的概率交互网络及其在蛋白质NMR谱图完全标记峰列表中的应用

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The process of assigning a finite set of tags or labels to a collection of observations, subject to side conditions, is notable for its computational complexity. This labeling paradigm is of theoretical and practical relevance to a wide range of biological applications, including the analysis of data from DNA microarrays, metabolomics experiments, and biomolecular nuclear magnetic resonance (NMR) spectroscopy. We present a novel algorithm, called Probabilistic Interaction Network of Evidence (PINE), that achieves robust, unsupervised probabilistic labeling of data. The computational core of PINE uses estimates of evidence derived from empirical distributions of previously observed data, along with consistency measures, to drive a fictitious system M with Hamiltonian H to a quasi-stationary state that produces probabilistic label assignments for relevant subsets of the data. We demonstrate the successful application of PINE to a key task in protein NMR spectroscopy: that of converting peak lists extracted from various NMR experiments into assignments associated with probabilities for their correctness. This application, called PINE-NMR, is available from a freely accessible computer server (). The PINE-NMR server accepts as input the sequence of the protein plus user-specified combinations of data corresponding to an extensive list of NMR experiments; it provides as output a probabilistic assignment of NMR signals (chemical shifts) to sequence-specific backbone and aliphatic side chain atoms plus a probabilistic determination of the protein secondary structure. PINE-NMR can accommodate prior information about assignments or stable isotope labeling schemes. As part of the analysis, PINE-NMR identifies, verifies, and rectifies problems related to chemical shift referencing or erroneous input data. PINE-NMR achieves robust and consistent results that have been shown to be effective in subsequent steps of NMR structure determination.
机译：在附带条件下，将一组有限的标签或标签分配给一组观测值的过程因其计算复杂性而闻名。该标记范例与广泛的生物学应用具有理论和实践意义，包括对DNA微阵列数据的分析，代谢组学实验和生物分子核磁共振（NMR）光谱。我们提出了一种新颖的算法，称为概率交互网络证据（PINE），该算法可实现健壮的，无监督的数据概率标记。 PINE的计算核心使用从先前观察到的数据的经验分布得出的证据估计以及一致性度量，将具有哈密顿量H的虚拟系统M推向准平稳状态，从而为数据的相关子集产生概率标签分配。我们证明了PINE在蛋白质NMR光谱分析中的一项关键任务的成功应用：将从各种NMR实验中提取的峰列表转换为与正确性相关的赋值的任务。可从可免费访问的计算机服务器（）上获得称为PINE-NMR的该应用程序。 PINE-NMR服务器接受蛋白质序列加上用户指定的数据组合作为输入，这些数据对应于一系列NMR实验；它提供了NMR信号（化学位移）到序列特异性主链和脂族侧链原子的概率分配以及蛋白质二级结构的概率确定结果。 PINE-NMR可以容纳有关赋值或稳定同位素标记方案的先验信息。作为分析的一部分，PINE-NMR可以识别，验证和纠正与化学位移参考或错误输入数据有关的问题。 PINE-NMR可获得鲁棒且一致的结果，已证明在随后的NMR结构确定步骤中有效。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

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