PrimAlign: PageRank-inspired Markovian alignment for large biological networks.
PrimAlign: PageRank-inspired Markovian alignment for large biological networks.
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DOI：
10.1093/bioinformatics/bty288
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发表时间：
2018-07-01
期刊：
影响因子：
--
通讯作者：
Cho YR
中科院分区：
文献类型：
--
作者：
Kalecky K;Cho YR
大规模蛋白质-蛋白质相互作用（PPI）网络的跨物种分析在理解细胞组织和功能进化的原理方面发挥了重要作用。最近，网络比对算法已经被提出来预测蛋白质的保守相互作用和功能。这些方法是基于这样的概念，即跨物种的直向同源蛋白是顺序相似的，并且直向同源物之间的PPI的拓扑结构通常是保守的。然而，网络对齐的高准确性和可扩展性仍然是一个挑战。我们提出了一种新的成对的全球网络对齐算法，称为PrimAlign，这是建模为马尔可夫链和迭代过渡，直到收敛。该算法还结合了PageRank的原则。这种方法是评估任务与人类，酵母和果蝇PPI网络。实验结果表明，PrimAlign优于几种流行的方法，在多个评估措施的统计显着差异。PrimAlign具有多平台性，其线性渐近时间复杂度使其在运行时具有上级性能。进一步的评估与合成网络和结果表明，流行的拓扑措施并不能反映真实的精度的路线。源代码可在http://web.ecs.baylor.edu/faculty/cho/PrimAlign上获得。 补充数据可在Bioinformatics在线获得。
Cross-species analysis of large-scale protein–protein interaction (PPI) networks has played a significant role in understanding the principles deriving evolution of cellular organizations and functions. Recently, network alignment algorithms have been proposed to predict conserved interactions and functions of proteins. These approaches are based on the notion that orthologous proteins across species are sequentially similar and that topology of PPIs between orthologs is often conserved. However, high accuracy and scalability of network alignment are still a challenge. We propose a novel pairwise global network alignment algorithm, called PrimAlign, which is modeled as a Markov chain and iteratively transited until convergence. The proposed algorithm also incorporates the principles of PageRank. This approach is evaluated on tasks with human, yeast and fruit fly PPI networks. The experimental results demonstrate that PrimAlign outperforms several prevalent methods with statistically significant differences in multiple evaluation measures. PrimAlign, which is multi-platform, achieves superior performance in runtime with its linear asymptotic time complexity. Further evaluation is done with synthetic networks and results suggest that popular topological measures do not reflect real precision of alignments. The source code is available at http://web.ecs.baylor.edu/faculty/cho/PrimAlign. Supplementary data are available at Bioinformatics online.
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影响因子：
5.8
作者：
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通讯作者：
Reinert, Knut
影响因子：
5.8
作者：
Neyshabur, Behnam;Khadem, Ahmadreza;Arab, Seyed Shahriar
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影响因子：
14.9
作者：
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Tyers M
Isorankn：多个蛋白质网络全局对齐的光谱方法。
DOI：
10.1093/bioinformatics/btp203
发表时间：
2009-06-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Liao CS;Lu K;Baym M;Singh R;Berger B
通讯作者：
Berger B
影响因子：
64.5
作者：
Rolland T;Taşan M;Charloteaux B;Pevzner SJ;Zhong Q;Sahni N;Yi S;Lemmens I;Fontanillo C;Mosca R;Kamburov A;Ghiassian SD;Yang X;Ghamsari L;Balcha D;Begg BE;Braun P;Brehme M;Broly MP;Carvunis AR;Convery-Zupan D;Corominas R;Coulombe-Huntington J;Dann E;Dreze M;Dricot A;Fan C;Franzosa E;Gebreab F;Gutierrez BJ;Hardy MF;Jin M;Kang S;Kiros R;Lin GN;Luck K;MacWilliams A;Menche J;Murray RR;Palagi A;Poulin MM;Rambout X;Rasla J;Reichert P;Romero V;Ruyssinck E;Sahalie JM;Scholz A;Shah AA;Sharma A;Shen Y;Spirohn K;Tam S;Tejeda AO;Wanamaker SA;Twizere JC;Vega K;Walsh J;Cusick ME;Xia Y;Barabási AL;Iakoucheva LM;Aloy P;De Las Rivas J;Tavernier J;Calderwood MA;Hill DE;Hao T;Roth FP;Vidal M
通讯作者：
Vidal M