A Novel Method Based on Greedy Algorithm for Informative SNP Selection

摘要

There are already lots of machine learning methods or combinatorial optimization algorithms for information SNP selection, but they still exist some problems, such as high computational complexity, less so on insufficient information. In order to overcome these problems, a new multiple loci LD measure is designed in the first stage. Then, we take the measure as a optimization object which is resolved by ant colony algorithm, so that these redundant loci are excluded. Compared with traditional methods, the proposed measure can not only more accurately describe the relationship between multiple sites, thus removing more redundant information, while optimization of the measure is significantly more economical than prediction optimization. During the refine phase, we use artificial neural networks as a learning model to reconstruct the genotype of non-information loci, and then to optimize the prediction accuracy by greedy algorithm. The greedy algorithm removes noise loci from candidate, so that it improves the accuracy and reduces the number of informative SNPs. The experimental results show that our method is effective.