ANMM4CBR：一种用于基因表达数据分类的基于案例的推理方法。
ANMM4CBR: a case-based reasoning method for gene expression data classification.
作者信息
Yao Bangpeng, Li Shao
机构信息
MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, PR China.
出版信息
Algorithms Mol Biol. 2010 Jan 6;5:14. doi: 10.1186/1748-7188-5-14.
BACKGROUND
Accurate classification of microarray data is critical for successful clinical diagnosis and treatment. The "curse of dimensionality" problem and noise in the data, however, undermines the performance of many algorithms.
METHOD
In order to obtain a robust classifier, a novel Additive Nonparametric Margin Maximum for Case-Based Reasoning (ANMM4CBR) method is proposed in this article. ANMM4CBR employs a case-based reasoning (CBR) method for classification. CBR is a suitable paradigm for microarray analysis, where the rules that define the domain knowledge are difficult to obtain because usually only a small number of training samples are available. Moreover, in order to select the most informative genes, we propose to perform feature selection via additively optimizing a nonparametric margin maximum criterion, which is defined based on gene pre-selection and sample clustering. Our feature selection method is very robust to noise in the data.
RESULTS
The effectiveness of our method is demonstrated on both simulated and real data sets. We show that the ANMM4CBR method performs better than some state-of-the-art methods such as support vector machine (SVM) and k nearest neighbor (kNN), especially when the data contains a high level of noise.
AVAILABILITY
The source code is attached as an additional file of this paper.
背景
微阵列数据的准确分类对于成功的临床诊断和治疗至关重要。然而，“维数灾难”问题和数据中的噪声会削弱许多算法的性能。
方法
为了获得一个鲁棒的分类器，本文提出了一种新颖的基于案例推理的加性非参数边际最大化（ANMM4CBR）方法。ANMM4CBR采用基于案例推理（CBR）方法进行分类。CBR是微阵列分析的合适范式，在微阵列分析中，由于通常只有少量训练样本可用，定义领域知识的规则很难获得。此外，为了选择最具信息性的基因，我们建议通过加性优化基于基因预选择和样本聚类定义的非参数边际最大化准则来进行特征选择。我们的特征选择方法对数据中的噪声非常鲁棒。
结果
我们的方法在模拟数据集和真实数据集上均得到了验证。我们表明，ANMM4CBR方法比一些先进方法（如支持向量机（SVM）和k近邻（kNN））表现更好，尤其是当数据包含高水平噪声时。
可用性
源代码作为本文的附加文件附上。
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