A novel forward gene selection algorithm for microarray data

作者：Du, Dajun^*; Li, Kang; Li, Xue; Fei, Minrui

来源：Neurocomputing, 2014, 133: 446-458.

DOI：10.1016/j.neucom.2013.12.012

• Computational complexity analysis
• Data augmentation
• Fast regression algorithm
• Gene selection
• Small samples
• Variant correlation

This paper investigates the gene selection problem for microarray data with small samples and variant correlation. Most existing algorithms usually require expensive computational effort, especially under thousands of gene conditions. The main objective of this paper is to effectively select the most informative genes from microarray data, while making the computational expenses affordable. This is achieved by proposing a novel forward gene selection algorithm (FGSA). To overcome the small samples' problem, the augmented data technique is firstly employed to produce an augmented data set. Taking inspiration from other gene selection methods, the L-2-norm penalty is then introduced into the recently proposed fast regression algorithm to achieve the group selection ability. Finally, by defining a proper regression context, the proposed method can be fast implemented in the software, which significantly reduces computational burden. Both computational complexity analysis and simulation results confirm the effectiveness of the proposed algorithm in comparison with other approaches.

• 出版日期2014-6-10
• 单位上海大学