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CEC
2003
IEEE
14 years 27 days ago
Stochastic neural network models for gene regulatory networks
AbstractRecent advances in gene-expression profiling technologies provide large amounts of gene expression data. This raises the possibility for a functional understanding of geno...
Tianhai Tian, Kevin Burrage
BMCBI
2010
98views more  BMCBI 2010»
13 years 7 months ago
A semi-nonparametric mixture model for selecting functionally consistent proteins
Background: High-throughput technologies have led to a new era of proteomics. Although protein microarray experiments are becoming more common place there are a variety of experim...
Lianbo Yu, R. W. Doerge
MICCAI
2005
Springer
14 years 8 months ago
Hybrid Segmentation Framework for Tissue Images Containing Gene Expression Data
Associating speci c gene activity with speci c functional locations in the brain anatomy results in a greater understanding of the role of the gene's products. To perform such...
Musodiq Bello, Tao Ju, Joe D. Warren, James Carson...
TCBB
2010
176views more  TCBB 2010»
13 years 6 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...
BMCBI
2007
126views more  BMCBI 2007»
13 years 7 months ago
Including probe-level uncertainty in model-based gene expression clustering
Background: Clustering is an important analysis performed on microarray gene expression data since it groups genes which have similar expression patterns and enables the explorati...
Xuejun Liu, Kevin K. Lin, Bogi Andersen, Magnus Ra...