Cancer classification and characteristic gene selection method

Abstract

The invention belongs to the field of biological information, and discloses a cancer classification and characteristic gene selection method, which comprises the following steps of: establishment of a primary learner: establishing T logistic regression models and a spark group lasso regularized loss function solving model corresponding to the T logistic regression models, and outputting a secondary learner training set; establishing a secondary learner: establishing a multi-response regression model and a loss function solving model corresponding to L1 regularization, and outputting a training set prediction result; and a prognosis feature selection model: establishing a prognosis feature selection SGL model. According to the cancer classification and feature gene selection method, the three standards of prediction, stabilization and selection are met, the accuracy and stability of the model on cancer classification prediction are improved through stacking integration, oncogenes and cancer-related genes are accurately selected, and the interpretability of the model is enhanced; gene and gene pathway priori knowledge are fused, and the accuracy of cancer classification and the effectiveness of feature selection are improved.

Description

technical field [0001] The invention relates to the field of biological information, in particular to a cancer classification and characteristic gene selection method. Background technique [0002] Numerous studies have demonstrated that genomics data are useful for classifying many cancers. With the development of sequencing technology, it is now possible to isolate and sequence genetic material from single cells. For such gene expression RNA-seq data, the number of variables p (as gene expression) is much larger than the sample size n. However, from a biological perspective, only a small subset of genetic variables strongly point to the targeted disease, whereas most genes are not associated with cancer classification. These irrelevant genes may introduce noise and reduce classification accuracy. Furthermore, from a machine learning perspective, too many genes may lead to overfitting and negatively impact classification performance, and the optimization process is not u...

AI Technical Summary

Problems solved by technology

For data sets with different feature group information, the traditional sparse group lasso (SGL) method has unstable prediction performance, and its feature selection ability depends on the selection of α

Images