相比之下，生物信息学的搜索系统使用的是一对算法，基本上是将一个基因组中的数据与另一个基因组中的数据进行比对。
Bioinformatics searches, by contrast, use a couple algorithms that basically compare the data from one genome to the data from another.
基因算法是对自然界生物进化的一种模仿，它包含三个基本的算子：再生、交叉和变异。
Containing three basic operators: reproduction, crossover and mutation, it imitates the evolution processes of natural organism.
聚类分析是基因数据分析中的一种重要手段，本文主要内容包括基于划分的聚类算法的改进以及模式聚类的理论研究。
Clustering is an important means to analyze genetic data. This paper focuses on the cluster analysis of gene chip data, including partitioned clustering and pattern-based approach.
由于在缺乏启发性的知识的情况下，遗传算法对于高维的数据的特征提取非常有效，所以遗传算法是基因数据研究中非常重要的算法。
Genetic algorithms are good candidates for attacking the challenge since GAs are very useful for extracting patterns in high-dimensionality problems where heuristic knowledge is sparse or incomplete.
由于在缺乏启发性的知识的情况下，遗传算法对于高维的数据的特征提取非常有效，所以遗传算法是基因数据研究中非常重要的算法。
Genetic algorithms are good candidates for attacking the challenge since GAs are very useful for extracting patterns in high-dimensionality problems where heuristic knowledge is sparse or incomplete.
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