大规模蛋白质相互作用网络复合物挖掘算法研究
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摘要
当前,生命医学研究正处于后基因组时代。系统地分析和全面理解蛋白质之间通过相互作用完成生命活动的规律成为最热的研究问题之一。特别是,从大规模蛋白质相互作用网络中识别蛋白质复合物对预测蛋白质功能、解释特定的生物进程具有重要意义。
针对算法CPM应用于蛋白质相互作用网络时,蛋白质复合物识别准确率不高等缺陷,我们通过引入距离限制条件约束蛋白质复合物的规模,进而提出了基于团渗透和距离限制的蛋白质复合物识别参数算法CPM-DR与非参数算法CP-DR。基于酵母蛋白质相互作用网络平台的实验结果表明,CPM-DR和CP-DR都比CPM能够更准确、更有效、更全面的识别出具有特定生物意义的蛋白质复合物。
针对传统的基于密度的局部搜索方法忽略了边缘蛋白质结点、稀疏的真实蛋白质复合物、没有考虑蛋白质对生命活动的基本差异等缺陷,提出了基于关键蛋白质和局部适应的蛋白质复合物识别算法EPOF,并将其应用到加权和非加权的酵母蛋白质相互作用网络。实验结果表明,EPOF相比于其他算法具有更好的性能。此外,EPOF能够识别具有生物意义的低密度蛋白质复合物。更进一步,EPOF验证了关键蛋白质在蛋白质复合物识别研究中具有极其重要的作用。
针对蛋白质相互作用的动态性、可利用的蛋白质相互作用数据的不完全性和存在噪声等众多问题,通过融合组织特异性的基因表达数据和人类静态蛋白质相互作用网络,提出了基于组织特异性和局部适应的蛋白质复合物识别算法TSOF。将TSOF应用到人类静态蛋白质相互作用网络的实验结果表明,TSOF识别的蛋白质复合物具有很强的生物意义。此外,TSOF验证了组织特异性蛋白质相互作用在蛋白质复合物识别研究中具有极其重要的作用。
本文提出的几个蛋白质复合物识别算法从不同角度出发,有效地解决了蛋白质相互作用网络聚类过程中存在的一些问题,识别的蛋白质复合物从统计意义上被证明是有生物意义的,对生物实验具有积极的指导意义。
Currently, the biomedical research is in the post-genome era. In the new era, one of the most important challenges is to systematically analyze and comprehensively understand how the proteins accomplish the life activities by interacting with each other. It plays an important role in predicting the protein functions and understanding specific biological processes that identify protein complexes from large-scale protein interaction networks.
The recognition accuracy of CPM is lowly and CPM is unfit to identify protein complexes with meso-scale when it applied in protein interaction networks. Thus, we introduced distance restriction, developed a novel parameterized algorithm called CPM-DR and a non-parameterized algorithm called CP-DR for identifying protein complexes. CPM-DR and CP-DR are applied to the protein interaction networks of Sacchromyces cerevisiae. The experiment results show that CPM-DR and CP-DR can detect a large number of protein complexes with specific biological significance and biological functions more effectively, more precisely and more comprehensively.
The traditional density-based and local search algorithms neglect many peripheral proteins that connect to the core protein clusters with few links when identify protein complexes. Thus, biologically meaningful protein complexes that do not have highly connected topologies are ignored. In addition, the previous methods for identifying protein complexes seldom take into account the fundamentality discrepancy of proteins to cellular life. However, the fact is that the importance of proteins to life activities is different. To overcome these disadvantages of previous algorithms, we propose a novel protein complex discovery algorithm based on Essential Protein and lOcal Fitness, named EPOF. The new algorithm EPOF is applied to the unweighted and weighted yeast protein interaction network. Experimental results show that EPOF outperforms other previous competing algorithms. In addition, EPOF could identify the significant protein complexes with low density. Moreover, EPOF verified the importance of essential proteins for identifying protein complexes.
Due to the dynamic nature of protein-protein interaction, the available protein interaction data high false positives and incomplete, we propose a new protein complex detecting algorithm based on Tissue-Specificity and lOcal Fitness (called TSOF) by integrating tissue-specificity gene expression data and human protein interaction network. TSOF is applied to the static human protein interaction network and the experiment results show that TSOF outperforms other previous competing algorithms. Moreover, TSOF verified the importance of tissue-specificity seed set, which is integrate biological character and network topological feature, for identifying protein complexes.
The protein complex mining algorithms proposed in this paper starts off from different angles and solves some problems effectively in the processes of clustering in protein interaction networks. The identified protein complexes are proved to be statistically significant, which can provide some references for biologists in their biochemical experiments.
针对算法CPM应用于蛋白质相互作用网络时,蛋白质复合物识别准确率不高等缺陷,我们通过引入距离限制条件约束蛋白质复合物的规模,进而提出了基于团渗透和距离限制的蛋白质复合物识别参数算法CPM-DR与非参数算法CP-DR。基于酵母蛋白质相互作用网络平台的实验结果表明,CPM-DR和CP-DR都比CPM能够更准确、更有效、更全面的识别出具有特定生物意义的蛋白质复合物。
针对传统的基于密度的局部搜索方法忽略了边缘蛋白质结点、稀疏的真实蛋白质复合物、没有考虑蛋白质对生命活动的基本差异等缺陷,提出了基于关键蛋白质和局部适应的蛋白质复合物识别算法EPOF,并将其应用到加权和非加权的酵母蛋白质相互作用网络。实验结果表明,EPOF相比于其他算法具有更好的性能。此外,EPOF能够识别具有生物意义的低密度蛋白质复合物。更进一步,EPOF验证了关键蛋白质在蛋白质复合物识别研究中具有极其重要的作用。
针对蛋白质相互作用的动态性、可利用的蛋白质相互作用数据的不完全性和存在噪声等众多问题,通过融合组织特异性的基因表达数据和人类静态蛋白质相互作用网络,提出了基于组织特异性和局部适应的蛋白质复合物识别算法TSOF。将TSOF应用到人类静态蛋白质相互作用网络的实验结果表明,TSOF识别的蛋白质复合物具有很强的生物意义。此外,TSOF验证了组织特异性蛋白质相互作用在蛋白质复合物识别研究中具有极其重要的作用。
本文提出的几个蛋白质复合物识别算法从不同角度出发,有效地解决了蛋白质相互作用网络聚类过程中存在的一些问题,识别的蛋白质复合物从统计意义上被证明是有生物意义的,对生物实验具有积极的指导意义。
Currently, the biomedical research is in the post-genome era. In the new era, one of the most important challenges is to systematically analyze and comprehensively understand how the proteins accomplish the life activities by interacting with each other. It plays an important role in predicting the protein functions and understanding specific biological processes that identify protein complexes from large-scale protein interaction networks.
The recognition accuracy of CPM is lowly and CPM is unfit to identify protein complexes with meso-scale when it applied in protein interaction networks. Thus, we introduced distance restriction, developed a novel parameterized algorithm called CPM-DR and a non-parameterized algorithm called CP-DR for identifying protein complexes. CPM-DR and CP-DR are applied to the protein interaction networks of Sacchromyces cerevisiae. The experiment results show that CPM-DR and CP-DR can detect a large number of protein complexes with specific biological significance and biological functions more effectively, more precisely and more comprehensively.
The traditional density-based and local search algorithms neglect many peripheral proteins that connect to the core protein clusters with few links when identify protein complexes. Thus, biologically meaningful protein complexes that do not have highly connected topologies are ignored. In addition, the previous methods for identifying protein complexes seldom take into account the fundamentality discrepancy of proteins to cellular life. However, the fact is that the importance of proteins to life activities is different. To overcome these disadvantages of previous algorithms, we propose a novel protein complex discovery algorithm based on Essential Protein and lOcal Fitness, named EPOF. The new algorithm EPOF is applied to the unweighted and weighted yeast protein interaction network. Experimental results show that EPOF outperforms other previous competing algorithms. In addition, EPOF could identify the significant protein complexes with low density. Moreover, EPOF verified the importance of essential proteins for identifying protein complexes.
Due to the dynamic nature of protein-protein interaction, the available protein interaction data high false positives and incomplete, we propose a new protein complex detecting algorithm based on Tissue-Specificity and lOcal Fitness (called TSOF) by integrating tissue-specificity gene expression data and human protein interaction network. TSOF is applied to the static human protein interaction network and the experiment results show that TSOF outperforms other previous competing algorithms. Moreover, TSOF verified the importance of tissue-specificity seed set, which is integrate biological character and network topological feature, for identifying protein complexes.
The protein complex mining algorithms proposed in this paper starts off from different angles and solves some problems effectively in the processes of clustering in protein interaction networks. The identified protein complexes are proved to be statistically significant, which can provide some references for biologists in their biochemical experiments.
引文
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