A new hybrid fractal algorithm for predicting thermophilic nucleotide sequences
A new hybrid fractal algorithm for predicting thermophilic nucleotide sequences
复制标题
一种用于预测嗜热核苷酸序列的新混合分形算法
DOI：
10.1016/j.jtbi.2011.09.028
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发表时间：
2012-01-21
影响因子：
2
通讯作者：
Hu, Dong-Gang
中科院分区：
文献类型：
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作者：
Lu, Jin-Long;Hu, Xue-Hai;Hu, Dong-Gang
一些最适生长温度（OGT）在50到80度之间的生物体的嗜热机制的知识在帮助设计稳定的蛋白质方面起着重要作用。如何预测一个DNA序列是嗜热的是一个长期但尚未完全解决的问题。混沌博弈表示（CGR）可以研究隐藏在DNA序列中的模式，并可以直观地揭示以前未知的结构。分形维数是测量复杂、高度不规则几何物体尺寸的好工具。本文利用CGR算法和分形维数将DNA序列转化为高维向量，然后利用这些分形特征和支持向量机（SVM）对DNA序列的热稳定性进行预测。我们对三组向量进行了实验：17维向量，65维向量和257维向量。每组通过10倍交叉验证测试进行评估。对于结果，257维向量组得到最好的结果：平均精度为0.9456，平均MCC为0.8878。结果也与以前的工作与单一CGR功能。实验结果表明，该算法具有较高的有效性。(C)2011爱思唯尔有限公司版权所有。
Knowledge of thermophilic mechanisms about some organisms whose optimum growth temperature (OGT) ranges from 50 to 80 degree plays a major role in helping design stable proteins. How to predict a DNA sequence to be thermophilic is a long but not fairly resolved problem. Chaos game representation (CGR) can investigate the patterns hiding in DNA sequences, and can visually reveal previously unknown structure. Fractal dimensions are good tools to measure sizes of complex, highly irregular geometric objects. In this paper, we convert every DNA sequence into a high dimensional vector by CGR algorithm and fractal dimension, and then predict the DNA sequence thermostability by these fractal features and support vector machine (SVM). We have conducted experiments on three groups: 17-dimensional vector, 65-dimensional vector, and 257-dimensional vector. Each group is evaluated by the 10-fold cross-validation test. For the results, the group of 257-dimensional vector gets the best results: the average accuracy is 0.9456 and average MCC is 0.8878. The results are also compared with the previous work with single CGR features. The comparison shows the high effectiveness of the new hybrid fractal algorithm. (C) 2011 Elsevier Ltd. All rights reserved.