Abstract: Genome wide expression analysis with DNA microarrays has become a mainstay of genomics research. The problem with microarrays is that there is no “gold standard” for microarray-generated data analysis. In most cases, traditional statistical and pattern recognition approaches are not productive in analysis of high dimension low sample size data analysis, and improvements, alternative approaches are currently being developed in order to effectively analyze and interpret microarray data. In this study we design and extend logiccombinatorial scheme that can be used in high-throughput gene expression data. Applied results show that proposed algorithm is able accurately discriminate different biological phenotypes, although some improvements should be further made.

Keywords: Pattern Recognition, Functional Pathways, gene expression, post traumatic stress disorder

ACM Classification Keywords: I.5 Pattern Recognition, I.5.2 Design Methodology

Link:

GROWING SUPPORT SET SYSTEMS IN ANALYSIS OF HIGH-THROUGHPUT GENE EXPRESSION DATA

Arsen Arakelyan, Anna Boyajian, Hasmik Sahakyan, Levon Aslanyan, Krassimira Ivanova, Iliya Mitov

http://foibg.com/ibs_isc/ibs-16/ibs-16-p06.pdf