| 摘要: |
| 针对结核病是导致全世界范围内人类发病的主要原因这个问题，寻找可用于结核病诊断和治疗的潜在分子标记物，提出通过微阵列分析比较活动性结核患者和潜伏性感染患者个体之间的外周血单核细胞的基因表达谱;利用ANOVA过滤出差异表达的基因，然后运用随机森林模型预测与结核病感染状态相关的基因，并与传统的支持向量回归模型对比表明随机森林回归模型具有较好的预测性能;最后选出排序前10的易感候选基因，文献查询结果表明这些基因都与结核病感染状态密切相关;对于识别不同的结核病感染程度或长期预防提供有效的参考。 |
| 关键词: 结核病 基因 方差分析 随机森林 |
| DOI： |
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| Identification of Key Genes Based on Gene Expression Profile of Tuberculosis |
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HE Lin1，CHEN Dong dong2，ZHANG Xu1，YANG Shao hua1
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| Abstract: |
| Tuberculosis is the main cause of human disease in the world. In order to find the biomarkers for the diagnosis and treatment of tuberculosis， we analyzed the gene expression profiles of peripheral blood mononuclear cells between individuals with active tuberculosis and latent infection by microarray analysis. First， we used ANOVA to filter out the differentially expressed genes and used the random forest model to predict the status of tuberculosis infection associated with the gene. Then， we compared it with the traditional support vector regression model and showed that the random forest regression model had better predictive performance. Finally， we selected the top 10 of the susceptible candidate genes， which were further proved by literature searching. These findings provide an effective reference for identifying different levels of tuberculosis infection or long term prevention. |
| Key words: tuberculosis gene ANOVA random forest |
