Abstract: Gene selection is a very important problem in microarray data analysis and has critical implications for the discovery of genes related to serious diseases. A fast algorithm based on Fisher Optimization Model (FOM) for gene selection was proposed. The computational complexity of the algorithm relies on the number of the samples not the huge number of genes. Experiments in public data demonstrated the proposed algorithm is effective and efficient.

Key words: microarray, gene selection, feature selection, fisher optimization model (FOM)

摘要： 基因选择是基因芯片数据分析中的一个重要问题。基因选择的主要困难在于基因数远远大于实验样本数。在Fisher优化模型的基础上，提出了快速Fisher优化模型，从而使得算法的计算规模主要依赖于样本数而不是特征数，大大提高了计算速度。在公共数据中的实验表明该方法速度快，选择的基因对分类结果是有效的。

关键词: 基因芯片, 基因选择, 特征选择, Fisher优化模型