基于遗传编程的蛋白质序列/spl α /-螺旋核心检测器的自动学习

Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI:10.1109/ICEC.1994.349904

S. Handley

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引用次数: 8

摘要

作者使用J.R. Koza(1992)的遗传编程来进化程序，将蛋白质的连续区域划分为/spl α /-螺旋核心或不是。他从一组90个蛋白质中剪切出/spl α /-螺旋核心区域的阳性和阴性例子。这些蛋白质是从布鲁克海文蛋白质数据库中选择的，是非同源的。程序的适应度定义为上述区域的观测值与预测值/spl α /-螺旋度之间的相关系数。由遗传编程系统产生的最适合的程序预测训练集至少和测试集一样好，观察到的分类与程序预测的分类(关于测试集中的蛋白质)之间的相关性为0.4818。

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Automated learning of a detector for the cores of /spl alpha/-helices in protein sequences via genetic programming

The author used J.R. Koza's (1992) genetic programming to evolve programs that classified contiguous regions of proteins as being /spl alpha/-helix cores or not. He snipped positive and negative examples of /spl alpha/-helix core regions out of a set of 90 proteins. These proteins were chosen from the Brookhaven Protein Data Bank to be non-homologous. The fitness of the programs was defined as the correlation coefficient between the observed and the predicted /spl alpha/-helicity of the above regions. The fittest program produced by the genetic programming system that predicted the training set at least as well as the testing set had a correlation of 0.4818 between the observed classifications and the classifications predicted by the program (on the proteins in the testing set).<>

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Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence

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