基于信息增益与遗传算法混合模型的最小数显著猪SNP选择
Selection of a Minimal Number of Significant Porcine SNPs by an Information Gain and Genetic Algorithm Hybrid Model
AI总结：
本文提出一种结合信息增益、遗传算法与特征频率选择的混合SNP筛选方法，用于猪品种分类，在仅保留0.86%的SNP时仍取得94.80%的分类准确率。
摘要：
分布于整个猪基因组的大量常见单核苷酸多态性（SNPs）组合已被广泛用于表征猪的遗传变异。随着SNP阵列技术的发展，可以容易地观测样本的全基因组遗传图谱。在大量此类变异中，存在一个远更小的SNP子集，能够同样用于正确识别相应品种。本工作提出了一种SNP选择启发式方法，该方法仍可有效用于品种分类过程。所提出的特征选择通过结合过滤式方法和包裹式方法——即信息增益方法与遗传算法——并附加一个特征频率选择步骤来完成，而分类则由支持向量机完成。该方法能够将猪数据集中显著SNP的数量减少到SNP总数的0.86%，并提供94.80%的高分类准确率。
英文摘要：
A panel of large number of common Single Nucleotide Polymorphisms (SNPs) distributed across an entire porcine genome has been widely used to represent genetic variability of pig. With the advent of SNP-array technology, a genome-wide genetic profile of a specimen can be easily observed. Among the large number of such variations, there exist a much smaller subset of the SNP panel that could equally be used to correctly identify the corresponding breed. This work presents a SNP selection heuristic that can still be used effectively in the breed classification process. The proposed feature selection was done by the approach of combining a filter method and a wrapper method--information gain method and genetic algorithm--plus a feature frequency selection step, while classification was done by support vector machine. The approach was able to reduce the number of significant SNPs to 0.86 % of the total number of SNPs in a swine dataset and provided a high classification accuracy of 94.80 %.
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