基因组组装是宏基因组分析的主要挑战之一。通常假设所有测序序列均来源于同一个基因组，微生物中非常活跃的可移动元件给这个前提假设提出了重大质疑。文章将该质疑抽象为可移动元件与宿主染色体之间的二分类问题，准确的二分类性能将进一步促进宏基因组学方面的研究。基于宏基因组测序数据的数值化特征，详细考察特征选择算法 ReliefF、卡方检验和 Fisher判别t检验，并结合分类模型逻辑回归、极限学习机、支持向量机和随机森林，验证最优可移动元件检测模型的性能。实验结果表明，ReliefF特征选择算法和随机森林分类算法的融合模型，使用100个特征即可正确分类95%以上的宏基因组测序数据，优于使用全部的690个特征。%Genome assembling is one of the challenges in metagenomic analysis. It is usually assumed that the sequencing reads are from the same genome. However, the mobile elements active in microbial genomes raise a critical question mark on this assumption. This work formulated this issue as a binary classiifcation problem. The accurate discrimination of mobile elements from chromosomes could greatly facilitate the metagenomic analysis. After quantifying the sequencing reads in metagenome, the collaboration of binary classiifcation algorithms with feature selection algorithms, including ReliefF, chi-squared test, and Fisher’st-test was investigated. All feature subsets were tested using the classiifcation algorithms such as logisitic regression, extreme learning machine, support vector machine and random forest. Experimental results demonstrate that the model based on ReliefF algorithm and Random Forest algorithm achieves over 95% in accuracy with only 100 features, which outperforms the model utilizing all 690 features.
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