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首页> 外文会议>2016 2nd International Conference of Signal Processing and Intelligent Systems >Combined mRMR filter and sparse Bayesian classifier for analysis of gene expression data
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Combined mRMR filter and sparse Bayesian classifier for analysis of gene expression data

机译：结合mRMR过滤器和稀疏贝叶斯分类器来分析基因表达数据

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摘要

Many disorders can be diagnosed by analysis of gene expression microarrays and this can save lots of lives. However, as gene expression data have high dimensions, establishing a method to identify the genes related to the target disease still remains a challenge, because it should provide a well-grounded prediction about the disease status. To this end, the best subset of genes should be distinguished for the classification task. In this paper, we have introduced a new framework for the analysis of gene expression data. Our proposed algorithm tries to find the best feature subset, in two main stages. First, an information theoretic forward feature selection algorithm called mRMR (minimum redundancy, maximum-relevancy) is used to find a candidate set for best features. In the next stage, the RVM (Relevance Vector Machine) classifier which is well suited for gene data analysis is utilized. The RVM has frequent privileges over other classifiers, namely, it can return a membership probability for each class that can be very vital for diagnosis of dramatic diseases, and it can also lead to a more sparse approach to fit a model over the training data which will help to avoid overfitting, etc. The Experimental results showed that the proposed algorithm outperforms the previous works in both classification accuracy and sparsity of the model.
机译：通过分析基因表达微阵列可以诊断出许多疾病，这可以挽救很多生命。然而，由于基因表达数据具有高维度，因此建立一种鉴定与目标疾病相关的基因的方法仍然是一个挑战，因为它应该提供有关疾病状况的充分依据的预测。为此，应该区分基因的最佳子集以进行分类任务。在本文中，我们介绍了一个用于分析基因表达数据的新框架。我们提出的算法试图在两个主要阶段中找到最佳特征子集。首先，使用一种称为mRMR（最小冗余，最大相关性）的信息理论前向特征选择算法来查找最佳特征的候选集。在下一阶段，将使用非常适合基因数据分析的RVM（相关向量机）分类器。 RVM相对于其他分类器具有频繁的特权，即，RVM可以返回每个类别的隶属概率，这对于诊断剧烈疾病非常重要，并且还可以导致一种更为稀疏的方法来对训练数据拟合模型实验结果表明，该算法在分类准确度和稀疏度两方面均优于先前的工作。

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