The prediction of protein interaction is one of the most important issues in post-genomic era.
蛋白质与蛋白质间相互作用的准确预测是计算分子生物学领域的重要研究内容。
These properties are similar to those of other extensively studied protein interaction networks.
这些性质类似于其他已经得到广泛研究过的蛋白质相互作用及其网络。
Bacterial two-hybrid system is a newly developed method for studying protein-protein interaction.
细菌双杂交系统是新近建立的一种研究蛋白质间相互作用的方法。
Research of RNA-protein interaction is important for the study of RNA regulation in gene expression. The.
要研究RNA对基因表达的调控就必须对RNA -蛋白质之间相互作用的方式进行研究。
The methods of multiple classifiers combination are proposed to classify protein-protein interaction sites.
提出将多分类器组合算法应用于蛋白质-蛋白质相互作用位点预测。
Information about the news, services, education and protein-protein interaction website is offered on the site.
网站同时提供新闻，服务，教育和蛋白质互作网站等信息。
Molecular elucidation of protein-protein interaction is essential for understanding the cellular dynamics and plasticity.
从分子机制上阐明蛋白质-蛋白质相互作用是了解细胞动力学和可塑性的基础。
Protein-protein interaction databases are thus becoming a major resource for investigating biological networks and pathways.
蛋白质-蛋白质相互作用数据库因此正在成为研究生物网络和通路的一种主要资源。
Study protein-protein interaction by native and denaturing gel electrophoresis, western blotting, and immunology precipitation.
研究蛋白质-蛋白质相互作用，由本地和变性凝胶电泳免疫印迹，免疫沉淀。
In vitro the interaction between BNP23 and PBP1 was ascertained by means of the protein-protein interaction and western blotting.
经体外蛋白质—蛋白质之间的相互作用和蛋白质印迹杂交方法进一步验证了PBP1和BNP23的相互作用；
Identification of protein-protein interaction sites is essential for the mutant design and prediction of protein-protein networks.
蛋白质相互作用位点的识别对于突变设计和预测蛋白质相互作用的网络是非常重要的。
Most potent drug target can be identifying among large number of non-homologous protein through protein interaction network analysis.
大多数有效的药物靶点能够通过蛋白质相互作用网络分析，在大量非同源蛋白质间识别。
Mayo Clinic researchers have discovered a protein interaction that may explain how the deadly Huntington's disease affects the brain.
Mayo诊所的研究者发现一种蛋白质的相互作用能够解释致命性亨廷顿病如何累及大脑。
Chapter four focused on identification of novel proteins involved in plant cell-wall synthesis based on protein-protein interaction data.
第四章讲述通过蛋白质相互作用数据来识别与植物细胞合成相关的新蛋白。
Prediction of protein-protein interaction sites is essential for mutant design and reconstruction of protein-protein interaction networks.
蛋白质相互作用位点的预测对于突变设计和蛋白质相互作用网络的重构都是至关重要的。
We then used a machine learning approach to deduce a protein interaction map that is most consistent with the underlying domain information.
为此我们对蛋白质序列进行了结构域的划分和映射，并采用机器学习的方法提取出结构域之间的相互作用。
We report a computational framework for prediction of CWSR proteins, based on known protein-protein interaction data and known CWSR proteins.
在此，我们报道如何利用蛋白相互作用数据和已知的细胞壁合成相关（CWSR）蛋白通过计算方法来预测新的细胞壁合成相关蛋白。
There has been no report on protein-protein interaction in Phanerochaete chrysosporium, a model microorganism for studying lignin degradation.
作为研究木质素降解系统的模式微生物黄孢原毛平革菌，迄今还没有蛋白-蛋白相互作用方面的报道。
Death domains and death domain-like protein interaction motifs in many proteins may bind each other or to some adaptor proteins for signaling.
含有死亡结构域或类死亡结构域的蛋白可以相互或与某些蛋白作用，从而进行信号传导。
AIM:To explore the protein-protein interactions of human mitochondrial intermembrane space(IMS) and construct the protein-protein interaction map.
目的：以人源线粒体膜间隙内蛋白为主要研究对象，发掘它们之间的相互作用，构建其相互作用网络。
Correlation coefficient of gene expression profiles for each pair of interaction proteins is calculated to filter the protein interaction network.
利用基因表达谱数据，通过计算互作蛋白质的表达相关系数，来筛选、优化蛋白质互作网络。
For example, through the large-scale identification of physical protein-protein interactions, comprehensive protein interaction maps are being generated.
例如，通过物理上蛋白质-蛋白质相互作用的大规模识别，产生了全面的蛋白质相互作用图谱。
The present studies on the characteristics of the membrane protein interaction network will be valuable for its relatively biological and medical studies.
针对这些膜蛋白相互作用及其网络特性的研究将有助于以后更深入开展一些相关生物和药物的研究。
Proteins potentially associated with the pathology of Alzheimers disease were gathered into our database, and were then mapped into a protein interaction network.
依据无标度网络的相关理论，提出一种预测蛋白质-蛋白质相互作用的算法，并预测潜在的作用位点。
Proteomics is a new field to research protein expression profiling and the protein-protein interaction. It must depend on high flux and high roboticized techniques.
蛋白质组学是旨在研究蛋白质表达谱和蛋白质与蛋白质之间相互作用的新领域，其研究必须依赖高通量、高自动化的技术。
By analyzing and researching protein-protein interaction network, scientists find that proteins which interact with each other tend to have similar cellular function.
科学家通过对蛋白相互作用网络的研究发现相互作用的蛋白质趋向于具有相似的细胞功能。
The researchers were able to sort the protein interaction pairs they found into functional groups, revealing networks and "communities" of proteins that work together.
研究人员把互作蛋白分类到功能组中用于揭示协同作用蛋白的网络。
Therefore, a unified standard of protein-protein interaction databases is in urgent need for collecting, collating the existing data and mining for useful information from them.
因此，目前亟需一个统一且规范的蛋白质相互作用数据库系统来收集和管理这些数据，并从已有的数据中挖掘有用信息。
Here, we provide reliable evidence that the size of protein interaction networks in different organisms appears to correlate much better with their apparent biological complexity.
本文我们提供可靠的证据，关于蛋白质相互作用网络规模的在不同有机体和它们明显的生物复杂性表现出相关要好的多的证据。
Here, we provide reliable evidence that the size of protein interaction networks in different organisms appears to correlate much better with their apparent biological complexity.
本文我们提供可靠的证据，关于蛋白质相互作用网络规模的在不同有机体和它们明显的生物复杂性表现出相关要好的多的证据。
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