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首页> 外文会议>Genetic and Evolutionary Computation Conference: A Joint Meeting of the Sixth Annual Genetic Programming Conference (GP-2001) and the Tenth International Conference on Genetic Algorithms (ICGA-2001), Jul 7-11, 2001, San Francisco, California >The Evaluation of a Stochastic Regular Motif Language for Protein Sequences
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The Evaluation of a Stochastic Regular Motif Language for Protein Sequences

机译：蛋白质序列的随机规则母题语言的评估

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摘要

A probabilistic regular motif language for protein sequences is evaluated. SRE-DNA is a stochastic regular expression language that combines characteristics of regular expressions and stochastic representations such as Hidden Markov Models. To evaluate its expressive merits, genetic programming is used to evolve SRE-DNA motifs for aligned sets of protein sequences. Different constrained grammatical forms of SRE-DNA expressions are applied to aligned protein sequences from the PROSITE database. Some sequences patterns were precisely determined, while others resulted in good solutions having considerably different features from the PROSITE equivalents. This research establishes the viability of SRE-DNA as a new representation language for protein sequence identification. The practicality of using grammatical genetic programming in stochastic biosequence expression classification is also demonstrated.
机译：评估蛋白质序列的概率规则基序语言。 SRE-DNA是一种随机正则表达式语言，它结合了正则表达式的特征和诸如隐马尔可夫模型的随机表示形式。为了评估其表达优势，使用遗传编程来发展用于序列对齐的蛋白质序列的SRE-DNA基序。 SRE-DNA表达的不同约束语法形式应用于来自PROSITE数据库的比对蛋白质序列。精确确定了一些序列模式，而另一些序列则导致了具有与PROSITE等效特征截然不同的良好解决方案。这项研究建立了SRE-DNA作为蛋白质序列鉴定的一种新的表示语言的可行性。还证明了在随机生物序列表达分类中使用语法遗传程序设计的实用性。

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