title: Informative gene selection and the direct classification of tumors based on relative simplicity.
abstract: BACKGROUND
  Selecting a parsimonious set of informative genes to build highly generalized performance classifier is the most important task for the analysis of tumor microarray expression data. Many existing gene pair evaluation methods cannot highlight diverse patterns of gene pairs only used one strategy of vertical comparison and horizontal comparison, while individual-gene-ranking method ignores redundancy and synergy among genes.
  
  
  RESULTS
  Here we proposed a novel score measure named relative simplicity (RS). We evaluated gene pairs according to integrating vertical comparison with horizontal comparison, finally built RS-based direct classifier (RS-based DC) based on a set of informative genes capable of binary discrimination with a paired votes strategy. Nine multi-class gene expression datasets involving human cancers were used to validate the performance of new method. Compared with the nine reference models, RS-based DC received the highest average independent test accuracy (91.40%), the best generalization performance and the smallest informative average gene number (20.56). Compared with the four reference feature selection methods, RS also received the highest average test accuracy in three classifiers (Naïve Bayes, k-Nearest Neighbor and Support Vector Machine), and only RS can improve the performance of SVM.
  
  
  CONCLUSIONS
  Diverse patterns of gene pairs could be highlighted more fully while integrating vertical comparison with horizontal comparison strategy. DC core classifier can effectively control over-fitting. RS-based feature selection method combined with DC classifier can lead to more robust selection of informative genes and classification accuracy.
authors.affiliation: Hunan Provincial Key Laboratory for Biology and Control of Plant Diseases and Insect Pests, Changsha, China. chenyuan0510@126.com.|Hunan Provincial Key Laboratory for Germplasm Innovation and Utilization of Crop, Hunan Agricultural University, Changsha, China. chenyuan0510@126.com.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Chen
authors.affiliation: Biotechnology Research Center, Hunan Academy of Agricultural Sciences, Changsha, China. wanglifeng112358@163.com.
authors.forename: Lifeng
authors.initials: L
authors.lastname: Wang
authors.affiliation: Hunan Provincial Key Laboratory for Germplasm Innovation and Utilization of Crop, Hunan Agricultural University, Changsha, China. lancy0829@163.com.
authors.forename: Lanzhi
authors.initials: L
authors.lastname: Li
authors.affiliation: Hunan Provincial Key Laboratory for Germplasm Innovation and Utilization of Crop, Hunan Agricultural University, Changsha, China. hongyan_zhang6@aliyun.com.
authors.forename: Hongyan
authors.initials: H
authors.lastname: Zhang
authors.affiliation: Hunan Provincial Key Laboratory for Biology and Control of Plant Diseases and Insect Pests, Changsha, China. zhmyuan@sina.com.|Hunan Provincial Key Laboratory for Germplasm Innovation and Utilization of Crop, Hunan Agricultural University, Changsha, China. zhmyuan@sina.com.
authors.forename: Zheming
authors.initials: Z
authors.lastname: Yuan
mesh_terms: D001499:Bayes Theorem; D030541:Databases, Genetic; D052138:Genes, Neoplasm; D006801:Humans; D008958:Models, Molecular; D009369:Neoplasms / Q000145:classification* / Q000175:diagnosis / Q000235:genetics*; D060388:Support Vector Machine