蛋白质相关文本分类的遗传编程

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI:10.1145/1569901.1570049

M. Segond, C. Fonlupt, D. Robilliard

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引用次数: 6

摘要

自基因组学革命以来，生物信息学从未如此受欢迎。许多研究人员已经成功地研究了进化计算在生物信息学中的应用[19]，例如在蛋白质折叠或确定基因组序列领域。在本文中，我们没有使用进化计算作为一种方法来为复杂的生物信息学问题提供新的和创新的解决方案，而是使用遗传编程作为一种工具来进化程序，该程序能够自动将研究论文分类为涉及或不涉及给定蛋白质。在第二部分中，我们展示了由遗传编程进化程序选择的属性可以有效地用于蛋白质分类。

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Genetic programming for protein related text classification

Since the genomics revolution, bioinformatics has never been so popular. Many researchers have investigated with great success the use of evolutionary computation in bioinformatics [19] for example in the field of protein folding or determining genome sequences. In this paper, instead of using evolutionary computation as a way to provide new and innovative solutions to complex bioinformatics problems, we use genetic programming as a tool to evolve programs that are able to automatically classify research papers as dealing or not with a given protein. In a second part, we show that the attributes that are selected by the genetic programming evolved programs can be used efficiently for proteins classification.

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Proceedings of the 11th Annual conference on Genetic and evolutionary computation

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