Analysis of binary feature mapping rules for promoter recognition in imbalanced DNA sequence datasets using Support Vector Machine

Robertas Damaševičius
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引用次数: 15

Abstract

Recognition of specific functionally-important DNA sequence fragments is considered one of the most important problems in bioinformatics. One type of such fragments are promoters, i.e., short regulatory DNA sequences located upstream of a gene. Detection of promoters in DNA sequences is important for successful gene prediction. In this paper, a machine learning method, called support vector machine (SVM), is used for classification of DNA sequences and promoter recognition. For optimal classification, 11 rules for mapping of DNA sequences into binary SVM feature space are analyzed. Classification is performed using a power series kernel function. Kernel parameters are optimized using a modification of the Nelder-Mead (downhill simplex) optimization method. The results of classification for drosophila and human sequence datasets are presented.
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基于支持向量机的非平衡DNA序列启动子识别二元特征映射规则分析
识别具有重要功能的DNA序列片段是生物信息学中最重要的问题之一。这种片段的一种类型是启动子,即位于基因上游的短调控DNA序列。DNA序列中启动子的检测对于成功的基因预测是非常重要的。本文采用支持向量机(SVM)作为机器学习方法,对DNA序列进行分类和启动子识别。为了实现最优分类,分析了DNA序列映射到二值支持向量机特征空间的11条规则。分类是使用幂级数核函数执行的。核参数的优化使用改进的Nelder-Mead(下坡单纯形)优化方法。介绍了果蝇和人类序列数据集的分类结果。
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