基于互信息算法的抗乳腺癌药物重定位分析

Breast Cancer Drugs Repositioning Based on Mutual Information

摘要: 针对抗乳腺癌药物重定位问题，首先在TCGA数据库中获取乳腺癌基因表达数据，然后通过互信息算法提取特征基因，最后通过connectivity map分析结果，将比对出的药物按照负相关的分值降序排列，得到gliquidone (格列喹酮)，Imatinib (格列卫)等可能对乳腺癌有治疗效果的药物。与传统药物相比，通过提取特征基因，找出对癌症基因有治疗效果的靶向治疗更具有针对性，通过重定位的方法筛选出抗乳腺癌药物不仅大大减少了新药开发的周期，还降低了经济成本。基于互信息算法提取特征基因为药物重定位提供了新的途径。

Abstract: In order to solve the problem of drug relocation for breast cancer, first, breast cancer gene expression data are gotten in the TCGA database, and then through the mutual information algorithm, the feature genes are extracted, finally, through connectivity map analysis results, the compared drugs are ranked in descending order according to the negative correlation scores, getting gliquidone and imatinib drugs that may have treatment effects on breast cancer. Compared with traditional drugs, targeted therapies that have therapeutic effects on cancer genes are more targeted by extracting characteristic genes. Screening anti-breast cancer drugs through retargeting method not only greatly reduces the cycle of new drug development, but also reduces the economic cost. Extracting feature genes based on mutual information algorithm provides a new way for drug relocalization.