These three types of database actually represent three graph objects, called the protein network, the gene universe and the chemical universe.
这三种类型的数据库实际上代表了三个图像对象，叫做蛋白质网络，基因总体和化学总体。
Along with the widespread of fractal concept and theory and its application in molecular biology, the research has been richened from the single protein to the protein network.
随着分形概念及理论的广泛应用和向分子生物学的渗透，人们从单个蛋白质的研究上升到对蛋白质网络的研究。
In this paper, we mainly propose a new evolving protein interaction network model charactered by anti-preference based on the basic principles of biological duplication and divergence.
本文主要根据生物学的复制和变异的基本原理，提出了具有反偏爱复制特性的一个蛋白质作用网络的新的演化模型。
Aim: To evaluate the application of serum protein fingerprint pattern based on artificial neural network in diagnosis of liver cancer.
目的：建立基于人工神经网络的血清蛋白质指纹图谱模型，并探讨其在肝癌诊断中的应用。
Among them, the most complex one is the signal transduction. These paths are organized non-linearly, and they can form a network through a series of protein interactions.
信号转导通路是非线性排列的，许多信号转导通路可以通过一系列的蛋白质与蛋白质相互作用形成一个网络。
To improve the prediction results of protein secondary structure, we developed a neural network ensemble model based on dual-layer feed forward BP network.
为了提高蛋白质二级结构预测精度，本文尝试采用一种基于串联BP网络集成的二级结构预测模型。
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。
The whole protein sequence and a neural network were used to predict protein structural classes.
利用神经网络和蛋白质全序列对蛋白质结构类进行了预测。
Repressible protein of axon growth mainly participated in regulating axon guidance, guiding axon growth and assisting construction of the precise neural network.
轴突生长抑制性蛋白在正常发育过程中主要参与调控轴突导向、引导轴突生长，协助构建精确的神经网络。
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 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。
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。
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。
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