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TCBB
2010
176views more  TCBB 2010»
13 years 2 months ago
Feature Selection for Gene Expression Using Model-Based Entropy
—Gene expression data usually contain a large number of genes, but a small number of samples. Feature selection for gene expression data aims at finding a set of genes that best...
Shenghuo Zhu, Dingding Wang, Kai Yu, Tao Li, Yihon...
CIARP
2009
Springer
13 years 11 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...
BMCBI
2007
173views more  BMCBI 2007»
13 years 4 months ago
Recursive Cluster Elimination (RCE) for classification and feature selection from gene expression data
Background: Classification studies using gene expression datasets are usually based on small numbers of samples and tens of thousands of genes. The selection of those genes that a...
Malik Yousef, Segun Jung, Louise C. Showe, Michael...
BMCBI
2010
183views more  BMCBI 2010»
13 years 2 months ago
The complexity of gene expression dynamics revealed by permutation entropy
Background: High complexity is considered a hallmark of living systems. Here we investigate the complexity of temporal gene expression patterns using the concept of Permutation En...
Xiaoliang Sun, Yong Zou, Victoria J. Nikiforova, J...
ICPR
2004
IEEE
14 years 5 months ago
Feature Selection and Gene Clustering from Gene Expression Data
In this article we describe an algorithm for feature selection and gene clustering from high dimensional gene expression data. The method is based on measuring similarity between ...
D. Dutta Majumder, Pabitra Mitra