• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

结构超指数的判别能力。

The discrimination power of structural SuperIndices.

机构信息

Institute for Bioinformatics and Translational Research, Hall in Tyrol, Austria.

出版信息

PLoS One. 2013 Jul 25;8(7):e70551. doi: 10.1371/journal.pone.0070551. Print 2013.

DOI:10.1371/journal.pone.0070551
PMID:23936227
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3723667/
Abstract

In this paper, we evaluate the discrimination power of structural superindices. Superindices for graphs represent measures composed of other structural indices. In particular, we compare the discrimination power of the superindices with those of individual graph descriptors. In addition, we perform a statistical analysis to generalize our findings to large graphs.

摘要

在本文中,我们评估了结构超指数的判别能力。图的超指数表示由其他结构指数组成的度量。特别地,我们比较了超指数与单个图描述符的判别能力。此外,我们进行了统计分析,将我们的发现推广到大型图。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f010/3723667/bc5e287a66d6/pone.0070551.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f010/3723667/e27b97f5f8a3/pone.0070551.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f010/3723667/fad77686b169/pone.0070551.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f010/3723667/3f16465c311b/pone.0070551.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f010/3723667/b409a4290ee2/pone.0070551.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f010/3723667/bc5e287a66d6/pone.0070551.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f010/3723667/e27b97f5f8a3/pone.0070551.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f010/3723667/fad77686b169/pone.0070551.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f010/3723667/3f16465c311b/pone.0070551.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f010/3723667/b409a4290ee2/pone.0070551.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f010/3723667/bc5e287a66d6/pone.0070551.g005.jpg

相似文献

1
The discrimination power of structural SuperIndices.结构超指数的判别能力。
PLoS One. 2013 Jul 25;8(7):e70551. doi: 10.1371/journal.pone.0070551. Print 2013.
2
Information indices with high discriminative power for graphs.具有高区分能力的图信息指标。
PLoS One. 2012;7(2):e31214. doi: 10.1371/journal.pone.0031214. Epub 2012 Feb 29.
3
New polynomial-based molecular descriptors with low degeneracy.基于多项式的低简并性分子描述符。
PLoS One. 2010 Jul 30;5(7):e11393. doi: 10.1371/journal.pone.0011393.
4
Quantitative descriptors of corneal shape derived from computer-assisted analysis of photokeratographs.从计算机辅助分析的角膜照相图得出的角膜形状的定量描述符。
Refract Corneal Surg. 1989 Nov-Dec;5(6):372-8.
5
A (sub)graph isomorphism algorithm for matching large graphs.一种用于匹配大型图的(子)图同构算法。
IEEE Trans Pattern Anal Mach Intell. 2004 Oct;26(10):1367-72. doi: 10.1109/TPAMI.2004.75.
6
Structural information content of networks: graph entropy based on local vertex functionals.网络的结构信息内容:基于局部顶点泛函的图熵
Comput Biol Chem. 2008 Apr;32(2):131-8. doi: 10.1016/j.compbiolchem.2007.09.007. Epub 2007 Sep 29.
7
Exact and approximate graph matching using random walks.使用随机游走的精确和近似图匹配
IEEE Trans Pattern Anal Mach Intell. 2005 Jul;27(7):1100-11. doi: 10.1109/tpami.2005.138.
8
Self-organizing maps for learning the edit costs in graph matching.用于学习图匹配中编辑成本的自组织映射。
IEEE Trans Syst Man Cybern B Cybern. 2005 Jun;35(3):503-14. doi: 10.1109/tsmcb.2005.846635.
9
Discrimination Power of Polynomial-Based Descriptors for Graphs by Using Functional Matrices.基于多项式的图描述符使用函数矩阵的判别能力
PLoS One. 2015 Oct 19;10(10):e0139265. doi: 10.1371/journal.pone.0139265. eCollection 2015.
10
Structural discrimination of networks by using distance, degree and eigenvalue-based measures.基于距离、度和特征值的网络结构歧视。
PLoS One. 2012;7(7):e38564. doi: 10.1371/journal.pone.0038564. Epub 2012 Jul 6.

本文引用的文献

1
Structural discrimination of networks by using distance, degree and eigenvalue-based measures.基于距离、度和特征值的网络结构歧视。
PLoS One. 2012;7(7):e38564. doi: 10.1371/journal.pone.0038564. Epub 2012 Jul 6.
2
Information indices with high discriminative power for graphs.具有高区分能力的图信息指标。
PLoS One. 2012;7(2):e31214. doi: 10.1371/journal.pone.0031214. Epub 2012 Feb 29.
3
Connections between classical and parametric network entropies.经典网络熵和参数网络熵之间的联系。
PLoS One. 2011 Jan 5;6(1):e15733. doi: 10.1371/journal.pone.0015733.
4
QuACN: an R package for analyzing complex biological networks quantitatively.QuACN:一个用于定量分析复杂生物网络的 R 包。
Bioinformatics. 2011 Jan 1;27(1):140-1. doi: 10.1093/bioinformatics/btq606. Epub 2010 Nov 11.
5
Novel topological descriptors for analyzing biological networks.用于分析生物网络的新型拓扑描述符。
BMC Struct Biol. 2010 Jun 17;10:18. doi: 10.1186/1472-6807-10-18.
6
A large scale analysis of information-theoretic network complexity measures using chemical structures.利用化学结构对信息论网络复杂性测度进行大规模分析。
PLoS One. 2009 Dec 15;4(12):e8057. doi: 10.1371/journal.pone.0008057.
7
Benchmark data set for in silico prediction of Ames mutagenicity.用于计算机模拟预测埃姆斯致突变性的基准数据集。
J Chem Inf Model. 2009 Sep;49(9):2077-81. doi: 10.1021/ci900161g.
8
On entropy-based molecular descriptors: statistical analysis of real and synthetic chemical structures.基于熵的分子描述符:真实和合成化学结构的统计分析。
J Chem Inf Model. 2009 Jul;49(7):1655-63. doi: 10.1021/ci900060x.
9
Biological network comparison using graphlet degree distribution.使用图let度分布进行生物网络比较。
Bioinformatics. 2007 Jan 15;23(2):e177-83. doi: 10.1093/bioinformatics/btl301.
10
Complexity of chemical graphs in terms of size, branching, and cyclicity.化学图在大小、分支和环状结构方面的复杂性。
SAR QSAR Environ Res. 2006 Aug;17(4):429-50. doi: 10.1080/10629360600884421.