Gene selection using a two-level hierarchical Bayesian model
Gene selection using a two-level hierarchical Bayesian model
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DOI：
10.1093/bioinformatics/bth419
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发表时间：
2004-12-12
期刊：
影响因子：
5.8
通讯作者：
Mallick, BK
中科院分区：
文献类型：
--
作者：
Bae, K;Mallick, BK
通过cDNA数据进行基因选择的基本问题是确定哪些基因在不同类型的组织样品（例如正常和癌症）中差异表达。cDNA数据包含大量的变量（基因），通常样本量相对较小，因此选择过程可能不稳定。因此，模型，将稀疏的变量（基因）是理想的这类问题。本文提出了一个两层层次贝叶斯模型的变量选择，假设先验，有利于稀疏。我们采用马尔可夫链蒙特卡罗（MCMC）为基础的计算技术来模拟参数的后验。该方法适用于白血病数据从以前的研究和乳腺癌的已发表的数据集。
The fundamental problem of gene selection via cDNA data is to identify which genes are differentially expressed across different kinds of tissue samples (e.g. normal and cancer). cDNA data contain large number of variables (genes) and usually the sample size is relatively small so the selection process can be unstable. Therefore, models which incorporate sparsity in terms of variables (genes) are desirable for this kind of problem. This paper proposes a two-level hierarchical Bayesian model for variable selection which assumes a prior that favors sparseness. We adopt a Markov chain Monte Carlo (MCMC) based computation technique to simulate the parameters from the posteriors. The method is applied to leukemia data from a previous study and a published dataset on breast cancer.