Identifying a small set of marker genes using minimum expected cost of misclassification

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• 作者：Samuel H. Huang^a ; ^{sam.huang@uc.edu} ; Dengyao Mo^a ; Jarek Meller^b ; Michael Wagner^c
• 关键词：Feature selection ; Expected cost of misclassification ; Sequential forward search ; Tag SNPs selection ; Breast cancer tumor classification
• 刊名：Artificial Intelligence in Medicine
• 出版年：2012
• 期刊代码：26_09333657
• 类别：med
• 出版时间：May, 2012
• 卷：55
• 期：1
• 页码：51-59
• 文件大小：433 K

摘要

Objectives

This paper presents a model independent feature selection approach to identify a small subset of marker genes.

Methods and material

An evaluation measure, minimum expected cost of misclassification (MEMC), is used to estimate the discriminative power of a feature subset without building a model. The MECM measure is combined with sequential forward search for feature selection. This approach was applied to a breast cancer profiling problem, with the goal of identifying a small number of marker genes whose expression can be used to predict cancer molecular subtype (p53 gene status). Furthermore, the method was also applied to find a small set of single-nucleotide polymorphisms (SNPs) that can be used to predict molecular phenotype of a different type, namely alleles (genetic variants) of human leukocyte antigen genes that play an important roles in autoimmunity.

Results

Two marker genes were identified based on p53 status, which achieved a p-value of 7.53 脳 10^鈭? (vs. 6 脳 10^鈭? with 32 genes identified by previous research) in survival analysis. Six SNP loci were identified that achieved a leave-one-out cross-validation accuracy of 92.8%(vs. 90.6%and 89.5%with 18 SNPs selected using 蠂² statistics and information gain, respectively).

Conclusion

The MECM-based feature selection approach is capable of identifying a smaller subset of market genes with comparable or even better performance than that obtained using conventional filter methods.