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» Classification of microarray data using gene networks
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119
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BMCBI
2010
96views more  BMCBI 2010»
15 years 3 months ago
sdef: an R package to synthesize lists of significant features in related experiments
Background: In microarray studies researchers are often interested in the comparison of relevant quantities between two or more similar experiments, involving different treatments...
Marta Blangiardo, Alberto Cassese, Sylvia Richards...
124
Voted
BMCBI
2007
104views more  BMCBI 2007»
15 years 3 months ago
Comparative evaluation of gene-set analysis methods
Background: Multiple data-analytic methods have been proposed for evaluating gene-expression levels in specific biological pathways, assessing differential expression associated w...
Qi Liu, Irina Dinu, Adeniyi J. Adewale, John D. Po...
136
Voted
CIARP
2009
Springer
15 years 10 months ago
Analysis of the GRNs Inference by Using Tsallis Entropy and a Feature Selection Approach
Abstract. An important problem in the bioinformatics field is to understand how genes are regulated and interact through gene networks. This knowledge can be helpful for many appl...
Fabrício Martins Lopes, Evaldo A. de Olivei...
110
Voted
CSB
2004
IEEE
131views Bioinformatics» more  CSB 2004»
15 years 7 months ago
Improved Fourier Transform Method for Unsupervised Cell-Cycle Regulated Gene Prediction
Motivation: Cell-cycle regulated gene prediction using microarray time-course measurements of the mRNA expression levels of genes has been used by several researchers. The popular...
Karuturi R. Krishna Murthy, Liu Jian Hua
128
Voted
ASUNAM
2010
IEEE
15 years 5 months ago
Semi-Supervised Classification of Network Data Using Very Few Labels
The goal of semi-supervised learning (SSL) methods is to reduce the amount of labeled training data required by learning from both labeled and unlabeled instances. Macskassy and Pr...
Frank Lin, William W. Cohen