Cellular automata and its applications in protein bioinformatics.
Cellular automata and its applications in protein bioinformatics.
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DOI：
10.2174/138920311796957720
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发表时间：
2011-08
影响因子：
2.8
通讯作者：
Xuan Xiao;Pu Wang;K. Chou
中科院分区：
文献类型：
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作者：
Xuan Xiao;Pu Wang;K. Chou
随着后基因组时代蛋白质序列的爆炸式增长，迫切需要开发高通量工具，仅根据序列信息快速可靠地鉴定未表征蛋白质的各种属性。由此获得的知识可以帮助我们及时利用这些新发现的蛋白质序列进行基础研究和药物发现。许多生物信息学工具都是通过机器学习方法开发出来的。本文综述了细胞自动机在蛋白质生物信息学中的应用。元胞自动机（CA）是一种开放、灵活和离散的动态模型，尽管模型本身很简单，但在复杂系统建模中具有巨大的潜力。来自不同领域的研究人员、科学家和实践者已经利用细胞自动机来可视化蛋白质序列，研究它们的进化过程，并预测它们的各种属性。由于其令人印象深刻的能力，直观性和相对简单，CA方法作为生物信息学工具具有巨大的潜力。
With the explosion of protein sequences generated in the postgenomic era, it is highly desirable to develop high-throughput tools for rapidly and reliably identifying various attributes of uncharacterized proteins based on their sequence information alone. The knowledge thus obtained can help us timely utilize these newly found protein sequences for both basic research and drug discovery. Many bioinformatics tools have been developed by means of machine learning methods. This review is focused on the applications of a new kind of science (cellular automata) in protein bioinformatics. A cellular automaton (CA) is an open, flexible and discrete dynamic model that holds enormous potentials in modeling complex systems, in spite of the simplicity of the model itself. Researchers, scientists and practitioners from different fields have utilized cellular automata for visualizing protein sequences, investigating their evolution processes, and predicting their various attributes. Owing to its impressive power, intuitiveness and relative simplicity, the CA approach has great potential for use as a tool for bioinformatics.