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CANDC
2005
ACM
13 years 8 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...
BMCBI
2007
182views more  BMCBI 2007»
13 years 9 months ago
EDISA: extracting biclusters from multiple time-series of gene expression profiles
Background: Cells dynamically adapt their gene expression patterns in response to various stimuli. This response is orchestrated into a number of gene expression modules consistin...
Jochen Supper, Martin Strauch, Dierk Wanke, Klaus ...
BIOINFORMATICS
2010
250views more  BIOINFORMATICS 2010»
13 years 7 months ago
DEGseq: an R package for identifying differentially expressed genes from RNA-seq data
Summary: High-throughput RNA sequencing (RNA-seq) is rapidly emerging as a major quantitative transcriptome profiling platform. Here we present DEGseq, an R package to identify di...
Likun Wang, Zhixing Feng, Xi Wang, Xiaowo Wang, Xu...
BIBE
2006
IEEE
139views Bioinformatics» more  BIBE 2006»
14 years 2 months ago
A Computational Inference Framework for analyzing Gene Regulation Pathway using Microarray Data
Microarray experiments produce gene expression data at such a high speed and volume that it is imperative to use highly specialized computational tools for their analyses. One grou...
Dong-Guk Shin, John Bluis, Yoo Ah Kim, Winfried Kr...
BMCBI
2008
129views more  BMCBI 2008»
13 years 9 months ago
Motif-directed network component analysis for regulatory network inference
Background: Network Component Analysis (NCA) has shown its effectiveness in discovering regulators and inferring transcription factor activities (TFAs) when both microarray data a...
Chen Wang, Jianhua Xuan, Li Chen, Po Zhao, Yue Wan...