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BMCBI
2005
190views more  BMCBI 2005»
13 years 5 months ago
An Entropy-based gene selection method for cancer classification using microarray data
Background: Accurate diagnosis of cancer subtypes remains a challenging problem. Building classifiers based on gene expression data is a promising approach; yet the selection of n...
Xiaoxing Liu, Arun Krishnan, Adrian Mondry
BMCBI
2010
116views more  BMCBI 2010»
13 years 5 months ago
FiGS: a filter-based gene selection workbench for microarray data
Background: The selection of genes that discriminate disease classes from microarray data is widely used for the identification of diagnostic biomarkers. Although various gene sel...
Taeho Hwang, Choong-Hyun Sun, Taegyun Yun, Gwan-Su...
ACIIDS
2010
IEEE
170views Database» more  ACIIDS 2010»
13 years 3 months ago
On the Effectiveness of Gene Selection for Microarray Classification Methods
Microarray data usually contains a high level of noisy gene data, the noisy gene data include incorrect, noise and irrelevant genes. Before Microarray data classification takes pla...
Zhongwei Zhang, Jiuyong Li, Hong Hu, Hong Zhou
AUSAI
2007
Springer
13 years 9 months ago
Hybrid Methods to Select Informative Gene Sets in Microarray Data Classification
Abstract. One of the key applications of microarray studies is to select and classify gene expression profiles of cancer and normal subjects. In this study, two hybrid approaches
Pengyi Yang, Zili Zhang
BMCBI
2010
153views more  BMCBI 2010»
13 years 5 months ago
Challenges in microarray class discovery: a comprehensive examination of normalization, gene selection and clustering
Background: Cluster analysis, and in particular hierarchical clustering, is widely used to extract information from gene expression data. The aim is to discover new classes, or su...
Eva Freyhult, Mattias Landfors, Jenny Önskog,...