基于邻域粗糙集的加权KNN肿瘤基因表达谱分类算法

肿瘤亚型的准确判别对肿瘤的治疗具有重要意义，对肿瘤的不同亚型进行准确判别是当前生物信息学研究的重要课题．本文首先利用Relief算法排序基因并选出初始的肿瘤信息基因子集，然后利用向基于邻域粗糙集模型的向前属性约减算法FARNeM来计算加权基因集合，最后用加权KNN算法对肿瘤对这些数据进行分析，从而发现有差异的基因表达。实验结果表明了上述方法的可行性和有效性。

Weighted KNN Algorithm for Tumor Gene Expression Profiles Classification Based on Neighborhood Rough Sets

The accurate identification of tumour subtypes in the treatment of tumors is important; the classification of different tumor subtypes has recently received a great deal of attention in the field of bioinformatics. The paper sorts genes using Relief algorithm and selects the initial subset of the genes of tumor information firstly. Then, calculates the weighted gene sets using the forward attribute reduction algorithm based on neighborhood rough set model. Then the weighted K-NN algorithm is used to analyze the data in order to detect differentially expressed genes. The results showed the feasibility and effectiveness of the method proposed in this paper.