期刊论文、会议论文

Yuan, Zheming

英文

BMC Medical Genomics

1755-8794

2013

6

1

1-14

International Conference on Bioinformatics and Computational Biology (BIOCOMP)

JUL 18-21, 2011

Las Vegas, NV

[Wang, Haiyan] Kansas State Univ, Dept Stat, Manhattan, KS 66506 USA.^[Zhang, Hongyan;Dai, Zhijun;Yuan, Zheming] Hunan Prov Key Lab Crop Germplasm Innovat & Util, Changsha 410128, Hunan, Peoples R China.^[Zhang, Hongyan] Hunan Agr Univ, Coll Informat Sci & Technol, Changsha 410128, Hunan, Peoples R China.^[Zhang, Hongyan;Dai, Zhijun;Yuan, Zheming] Hunan Agr Univ, Coll Bio Safety Sci & Technol, Changsha 410128, Hunan, Peoples R China.^[Chen, Ming-shun] USDA ARS, Manhattan, KS 66506 USA.^[Chen, Ming-shun] Kansas State Univ, Dept Entomol, Manhattan, KS 66506 USA.

CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND

BMC

10.1186/1755-8794-6-S1-S3

The research was supported by a grant from the Science Foundation for Distinguished Young Scholars of Hunan Province, China (No. 10JJ1005) to ZY, the Scientific Research Fund of Hunan Provincial Education Department (No. 11C0654) to HZ, and a grant from the Simons Foundation to HW (#246077). This article has been published as part of BMC Medical Genomics Volume 6 Supplement 1, 2013: Proceedings of the 2011 International Conference on Bioinformatics and Computational Biology (BIOCOMP’11). The full contents of the supplement are available online at http://www.biomedcentral.com/ bmcmedgenomics/supplements/6/S1. Publication of this supplement has been supported by the International Society of Intelligent Biological Medicine.

本校为其他机构

One of the challenges in classification of cancer tissue samples based on gene expression data is to establish an effective method that can select a parsimonious set of informative genes. The Top Scoring Pair (TSP), k-Top Scoring Pairs (k-TSP), Support Vector Machines (SVM), and prediction analysis of microarrays (PAM) are four popular classifiers that have comparable performance on multiple cancer datasets. SVM and PAM tend to use a large number of genes and TSP, k-TSP always use even number of genes. In addition, the selection of distinct gene pairs in k-TSP simply combined the pairs of top ...