Designing a scoring function plays a key role in protein structure prediction.
评分函数设计是预测蛋白质结构的关键之一。
After that there will be a down time while we implement new methods of protein structure prediction.
之后将有一段停机时期，我们将实现新的蛋白质预测方法。
Protein structure prediction has proven to be one of the central problems in the field of computational biology.
蛋白质结构预测问题是计算生物学领域的核心问题之一。
The experimental results from the protein structure prediction demonstrate that the model is effective and promising.
实验研究结果表明本文所提出的模型是有效的并具有良好的发展前景。
Protein structure prediction problem is to predict the dimensional folding configurations from its amino acid sequence.
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。
Numerical tests illustrate that the memory Tabu search algorithm is feasible and effective to protein structure prediction problems.
数值实验表明该算法对于蛋白质结构预测是可行有效的。
Using a new neighborhood structure and partly randomized off-trap strategy, a novel local search algorithm for protein structure prediction is proposed.
构造了新的邻域结构，采用了部分随机跳坑策略，对此问题提出了新的局部搜索算法。
Research on conformational space of protein structure in which proteins take clear regulation plays a key role in protein structure prediction and simulation.
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。
The biological sequence analysis research content mainly includes the sequence alignment, the protein structure prediction, and the genome sequence analysis etc.
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。
We are also currently competing with a Foldit team in CASP9, the main competition for evaluating protein structure prediction methods on unreleased protein structures.
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。
Some methods are reviewed and different states of protein structure prediction are introduced, and some difficulties in protein structure prediction are put forward.
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。
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.
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。
Multiple sequence alignment is one of the essential tools of studying bioinformatics and it plays an important role in the evolution analysis and protein structure prediction.
多序列比对是一种重要的生物信息学工具，在生物的进化分析以及蛋白质的结构预测方面有著重要的应用。
Similarity searches are a powerful method for solving important biological problems such as database scanning, evolutionary studies, gene prediction, and protein structure prediction.
相似性搜索是解决重要的生物学问题，例如数据库扫描、进化研究、基因预测和蛋白质结构预测的一种强大的方法。
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.
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。
Protein structure prediction has matured over the past few years to the point that even fully automated methods can provide reasonably accurate three-dimensional models of protein structures.
蛋白质结构预测在过去的几年里已经成熟到甚至完全自动的方法就能够提供相当准确的蛋白质结构的三维模型的程度。
They will then be doing cutting-edge research in protein-structure prediction.
随后玩家们将参与到蛋白结构预测这一尖端课题的研究中。
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.
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。
Prediction of lowly homological protein secondary structure is still a difficult problem up to now.
低同源蛋白质的二级结构预测至今仍然是一个困难的问题。
The objective function in the model of prediction of protein structure is a potential energy function, either physics-based potentials or statistic-based potentials.
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 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.
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 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.
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 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.
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 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。
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。
Another research subject is the prediction of protein secondary structure.
论文的另一个研究主题是蛋白质二级结构的预测方法。
Protein secondary structure prediction becomes the most important step of predicting the space conformation from protein molecule.
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。
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.
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。
With the completion of human genome project, prediction and analysis of protein structure and function by using informatics has become one of the most important tasks of after the genome project.
随着人类基因组计划的完成，应用生物信息学技术预测蛋白质结构与功能将成为后基因组时代的一项重要任务。
In the paper, an improved TS algorithm is proposed for protein Three-Dimensional (3d) folding structure prediction in AB off-lattice model.
基于AB非格模型，该文将一种改进的禁忌搜索算法应用于蛋白质三维折叠结构预测。
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