关键词: DNA甲基化, 癌症基因组图谱（TCGA）, 合成少数类采样技术（SMOTE）, Tomek Link算法, gcForest算法

Abstract:

As an important epigenetic phenomenon in the human genome, DNA methylation is playing an important regulatory role in gene expression and is closely related to cancer. Aiming at the class imbalance and high dimension of the huge data of The Cancer Genome Atlas（TCGA）, the problem of large increase in the false negative rate is proposed. A mixed sampling unbalanced data integration classification algorithm is proposed, which is generated by Synthetic Minority Over-sampling Technique（SMOTE） algorithm. A new sample of the minority class, the extended data set, through the Tomek Link algorithm to eliminate the noise introduced in the sample expansion process, resulting in a relatively balanced data set. On this basis, using the cascade forest structure of the deep forest（gcForest）, two random forest structures are chosen for each layer to enhance the generalization ability of the model and obtain the final classification model. Experiments on DNA methylation data of 6 kinds of cancers show that the mixed sampling and unbalanced data integration classification algorithm effectively improve the sensitivity to a few classes under the premise of ensuring the classification accuracy of most classes.

Key words: DNA methylation, The Cancer Genome Atlas（TCGA）, Synthetic Minority Oversampling Technique（SMOTE）, Tomek Link algorithm, gcForest algorithm