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IDA
2005
Springer
14 years 2 months ago
Bayesian Networks Learning for Gene Expression Datasets
DNA arrays yield a global view of gene expression and can be used to build genetic networks models, in order to study relations between genes. Literature proposes Bayesian network ...
Giacomo Gamberoni, Evelina Lamma, Fabrizio Riguzzi...
BMCBI
2010
132views more  BMCBI 2010»
13 years 8 months ago
Error margin analysis for feature gene extraction
Background: Feature gene extraction is a fundamental issue in microarray-based biomarker discovery. It is normally treated as an optimization problem of finding the best predictiv...
Chi Kin Chow, Hai Long Zhu, Jessica Lacy, Winston ...
BMCBI
2008
130views more  BMCBI 2008»
13 years 8 months ago
Function approximation approach to the inference of reduced NGnet models of genetic networks
Background: The inference of a genetic network is a problem in which mutual interactions among genes are deduced using time-series of gene expression patterns. While a number of m...
Shuhei Kimura, Katsuki Sonoda, Soichiro Yamane, Hi...
BMCBI
2007
149views more  BMCBI 2007»
13 years 8 months ago
Robust imputation method for missing values in microarray data
Background: When analyzing microarray gene expression data, missing values are often encountered. Most multivariate statistical methods proposed for microarray data analysis canno...
Dankyu Yoon, Eun-Kyung Lee, Taesung Park
BMCBI
2008
124views more  BMCBI 2008»
13 years 8 months ago
Literature-aided meta-analysis of microarray data: a compendium study on muscle development and disease
Background: Comparative analysis of expression microarray studies is difficult due to the large influence of technical factors on experimental outcome. Still, the identified diffe...
Rob Jelier, Peter A. C. 't Hoen, Ellen Sterrenburg...