Mining Classifying Rules from Tumor Gene Expression Data

Establishing tumor prediction and classification models using methodology and technology of information science based on the tumor gene expression data is meaningful to the research of tumor gene expression patterns identification and tumor diagnosis and recognition as well.This paper presented a method to construct tumor classifier using the classifying rules directly mined from tumor gene expression data.According to this method,we extracted the experiment sample dataset and then searched classifying features that could respectively mark the tumor and normal sample from this dataset.Based on the classifying features mined,the classifying rules were generated and used to predict each unknown sample according to the principle of highest confidence.The experiment made on the prostate cancer gene expression data from Broad Institute showed that the prediction accuracy of this method was over 90% and a lot of classifying rules with transparent prediction structure were generated at the same time.The experimental results proved the feasibility and effectiveness of this method.