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» Gene Classification using Expression Profiles: A Feasibility...
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BMCBI
2006
122views more  BMCBI 2006»
13 years 7 months ago
A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets
Background: Gene selection is an important step when building predictors of disease state based on gene expression data. Gene selection generally improves performance and identifi...
Carmen Lai, Marcel J. T. Reinders, Laura J. van't ...
BMCBI
2005
116views more  BMCBI 2005»
13 years 7 months ago
Dynamic covariation between gene expression and proteome characteristics
Background: Cells react to changing intra- and extracellular signals by dynamically modulating complex biochemical networks. Cellular responses to extracellular signals lead to ch...
Mansour Taghavi Azar Sharabiani, Markku Siermala, ...
CANDC
2005
ACM
13 years 7 months ago
Gene selection from microarray data for cancer classification - a machine learning approach
A DNA microarray can track the expression levels of thousands of genes simultaneously. Previous research has demonstrated that this technology can be useful in the classification ...
Yu Wang 0008, Igor V. Tetko, Mark A. Hall, Eibe Fr...
ACIIDS
2010
IEEE
170views Database» more  ACIIDS 2010»
13 years 5 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
APBC
2004
121views Bioinformatics» more  APBC 2004»
13 years 9 months ago
Using Emerging Pattern Based Projected Clustering and Gene Expression Data for Cancer Detection
Using gene expression data for cancer detection is one of the famous research topics in bioinformatics. Theoretically, gene expression data is capable to detect all types of early...
Larry T. H. Yu, Fu-Lai Chung, Stephen Chi-fai Chan...