Study on Leukemia Molecular Prediction Model with Gene Expression Profile
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摘要: 采用生物信息学方法对肿瘤基因表达数据进行挖掘,以获取和肿瘤不同亚型相关的候选标志基因集合,应用机器学习方法从标志基因集合中提取出甄别肿瘤不同亚型的规则集,进而建立起肿瘤预测模型.利用Relief、信息增益和分类信息指数从不同角度挖掘蕴含在基因表达谱中的候选特征基因,抽取出候选特征基因公约集合.以对不同肿瘤组织样本的识别能力为依据,选取分类能力最强的一组基因集合作为特征基因.利用规则判定树提取出反映这些特征基因相互作用的规则集并以此构建肿瘤预测模型,并将此模型应用于白血病基因表达数据中,建立了白血病分子预测模型.研究表明,该模型得到的白血病标志基因对肿瘤临床诊断具有一定的参考价值.Abstract: A leukemia molecular prediction model is constructed by using bioinformatics and machine learning methods with gene expression profile.Firstly,three methods including relief,classification information index and information gain index are used to select candidate feature gene set from the leukemia gene expression profile.Secondly,intersection of three candidate feature gene sets is generated,and then the best classification performance of intersection genes which is tested by SVM is selected as feature genes.Thirdly, the classification rule sets are extracted from these feature genes by using decision tree method.Finally,the leukemia molecular prediction model is constructed with these classification rules.The results show that the model is helpful to cancer clinical diagnosis and cancer gene biological experiments.Also,the two key genes (CD33,MPO)are biomarkers of leukemia clinically.
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Keywords:
- tumor /
- gene expression profile /
- decision tree /
- support vector machine
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