即使我们能抓住蛋白质折叠的规律，下一步就是通过分析数百万蛋白质和其它分子之间的相互作用，来预测细胞的结构。
Even if we could solve protein-folding, the next stage would be to predict the structure of cells on the basis of the interactions of millions of proteins and other molecules.
我们现在也在和一个代号为CASP9的游戏小组进行比赛，主要比赛项目是评估蛋白质预测方法在未公布的蛋白质结构上实施的可行性。
We are also currently competing with a Foldit team in CASP9, the main competition for evaluating protein structure prediction methods on unreleased protein structures.
在2003年，他预测到了DNA和蛋白质测序和分析成本的下降和蛋白质结构测定步伐的加快(r . Carlson Biosecur . Bioterror . 1, 203 - 214； 2003)。
In 2003, he projected the falling costs of sequencing and synthesis of DNA and proteins, and the accelerating pace of research into areas such as protein structure determination .
文章针对蛋白质二级结构预测这一复杂非线性模式分类问题，提出了基于径向基函数的预测方法。
Aiming at solving the complicated non-linear pattern classification problem of protein secondary structure prediction, a new method based on radial basis function is proposed.
蛋白质结构预测模型的目标函数通常采用基于物理理论的经验势函数或基于统计理论的平均势能函数。
The objective function in the model of prediction of protein structure is a potential energy function, either physics-based potentials or statistic-based potentials.
评分函数设计是预测蛋白质结构的关键之一。
Designing a scoring function plays a key role in protein structure prediction.
生物序列分析的主要研究内容包括序列比对、蛋白质结构预测、基因组序列分析等。
The biological sequence analysis research content mainly includes the sequence alignment, the protein structure prediction, and the genome sequence analysis etc.
简要地介绍了模拟退火算法，遗传算法，人工神经网络和图论算法在蛋白质结构预测中的应用。
The paper briefly introduces some modern optimum algorithms of simulated annealing, genetic algorithms, neural networks and graphic algorithms in and applied in prediction of protein structure.
应用这个方法对蛋白质结构的二硫键进行了预测并取得了良好的结果。
This method has being used to predict disulfide bonding in protein structure and a fine result has been got.
蛋白质结构预测问题是计算生物学领域的核心问题之一。
Protein structure prediction has proven to be one of the central problems in the field of computational biology.
用电子计算机方法可以预测球形蛋白质二级结构。
A computerized method is made to predict the secondary structure of globular proteins.
本文中，我们利用数据挖掘得到的统计信息数据库对蛋白质的二级结构进行了预测。
However this process is quite tough. Here our statistical information database by data mining is used for predicting protein secondary structure.
利用氨基酸序列预测蛋白质结构可以归结为一个复杂系统的全局优化问题，建立一个合理的预测模型是关键性的第一步。
The ab initio prediction of protein structure is to solve a global optimization problem per se, in which the first step is to build a mathematical model.
氨基酸序列决定蛋白质高级结构是普遍接受的假设，也是蛋白质结构预测的理论基础。
It is generally accepted that amino acid sequences determine the spatial structures of proteins. Protein structure prediction methods are explicitly or implicitly based on this assumption.
蛋白质结构预测是生物信息学的一个主要研究方向，而蛋白质关联图预测是其中的一个重要内容。
The protein structure prediction is a main direction in bioinformatics, and the prediction of protein contact maps is an important content in protein structure prediction.
分析了多分类器融合算法的理论框架，并采用决策模板算法对蛋白质结构类的预测问题进行了研究。
We investigate the theoretical framework of multiple classifiers fusion, and apply the decision template algorithms to classify the protein secondary structural classes.
该文简要介绍图的连通子图、图的最大团、图的完美匹配及图谱法在蛋白质结构预测中的应用。
The paper briefly introduces some graph theory methods such as maximal connected subgraph, maximal cliques, perfect match and graph spectral research which applied in prediction of protein structure.
蛋白质构形预测问题就是根据组成蛋白质的氨基酸序列来预测其空间折叠结构。
Protein structure prediction problem is to predict the dimensional folding configurations from its amino acid sequence.
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。
To improve the prediction results of protein secondary structure, we developed a neural network ensemble model based on dual-layer feed forward BP network.
在线用蛋白质二级结构预测软件分析MAGE - 4抗原表位，PCR获取MAGE - 4抗原特征表位基因。
Analyze MAGE-4 antigen epitope by online predict software of protein secondary structure and amplify the epitope gene by PCR.
蛋白质结构预测是生物信息学中的重要课题，而蛋白质序列是蛋白质结构预测的基础。
The structure prediction of proteins is the important problem of biology informatics. And the protein sequence is the base of the structure prediction of proteins.
蛋白质结构构象呈现明显的规律，研究其在特定构象空间的分布对蛋白质结构预测和模拟具有重要意义。
Research on conformational space of protein structure in which proteins take clear regulation plays a key role in protein structure prediction and simulation.
数值实验表明该算法对于蛋白质结构预测是可行有效的。
Numerical tests illustrate that the memory Tabu search algorithm is feasible and effective to protein structure prediction problems.
详细地综述了几种方法，并简单地介绍了蛋白质结构预测的几个不同阶段，并提出了在蛋白质结构预测方面存在的一些困难。
Some methods are reviewed and different states of protein structure prediction are introduced, and some difficulties in protein structure prediction are put forward.
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。
The whole protein sequence and a neural network were used to predict protein structural classes.
采用非格点模型，以相对熵作为优化函数，进行蛋白质三维结构预测。
The off-lattice model is adopted and the relative entropy is used as a minimization function to predict the tertiary structure of a protein.
该项目的目标是“寻找能够准确预测和设计蛋白质结构及聚合物的方法，希望能最终帮助研究者们找到治愈人类疾病的途径”。
Its goal is "… to develop methods that accurately predict and design protein structures and complexes, an endeavor that may ultimately help researchers develop cures for human diseases."
蛋白质二级结构预测是蛋白质结构预测的重要组成部分，是蛋白质结构预测最关键的步骤。
Protein secondary structure prediction becomes the most important step of predicting the space conformation from protein molecule.
对国内外近年来应用这些方法在蛋白质3d结构预测及折叠的研究工作进行了回顾，并分析、比较了这几种方法的效果和特点。
Recently it applied on the problem of protein folding and 3d structure prediction using these methods are then reviewed. The effect and feature of these methods are also analyzed and compared.
本文主要论述了神经网络在预测蛋白质二级结构方面的进展以及存在的问题。
Progress and problems in the field of predicting protein secondary structure by neural networks are presented in this paper.
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