多吸引子元胞自动机（MACA）在生物信息学中的应用
Multiple Attractor Cellular Automata (MACA) for Addressing Major Problems in Bioinformatics
摘要：
CA已经成为解决生物信息学中主要问题的潜在分类器。许多生物信息学问题，如蛋白质编码区预测、启动子区寻找、蛋白质结构预测等，都可以通过元胞自动机来解决。尽管有一些预测技术可以解决这些问题，但近似的准确性水平很低，提出了一种自动化的过程与MACA（多吸引子元胞自动机），可以解决所有这些问题。遗传算法也被用来寻找具有良好适应值的规则。进行了大量的实验，报告所提出的工具的准确性。使用ENCODE、BG 570、HMR 195、Fickett和Tongue、ASP 67数据集进行测试时，MACA的平均准确率为78%。
英文摘要：
CA has grown as potential classifier for addressing major problems in bioinformatics. Lot of bioinformatics problems like predicting the protein coding region, finding the promoter region, predicting the structure of protein and many other problems in bioinformatics can be addressed through Cellular Automata. Even though there are some prediction techniques addressing these problems, the approximate accuracy level is very less. An automated procedure was proposed with MACA (Multiple Attractor Cellular Automata) which can address all these problems. The genetic algorithm is also used to find rules with good fitness values. Extensive experiments are conducted for reporting the accuracy of the proposed tool. The average accuracy of MACA when tested with ENCODE, BG570, HMR195, Fickett and Tongue, ASP67 datasets is 78%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
