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BMCBI
2006
123views more  BMCBI 2006»
13 years 4 months ago
Characterizing disease states from topological properties of transcriptional regulatory networks
Background: High throughput gene expression experiments yield large amounts of data that can augment our understanding of disease processes, in addition to classifying samples. He...
David Tuck, Harriet Kluger, Yuval Kluger
IEAAIE
2010
Springer
13 years 2 months ago
Constructive Neural Networks to Predict Breast Cancer Outcome by Using Gene Expression Profiles
Abstract. Gene expression profiling strategies have attracted considerable interest from biologist due to the potential for high throughput analysis of hundreds of thousands of gen...
Daniel Urda, José Luis Subirats, Leonardo F...
BMCBI
2005
131views more  BMCBI 2005»
13 years 4 months ago
Regularized Least Squares Cancer Classifiers from DNA microarray data
Background: The advent of the technology of DNA microarrays constitutes an epochal change in the classification and discovery of different types of cancer because the information ...
Nicola Ancona, Rosalia Maglietta, Annarita D'Addab...
BMCBI
2006
198views more  BMCBI 2006»
13 years 4 months ago
Gene selection and classification of microarray data using random forest
Background: Selection of relevant genes for sample classification is a common task in most gene expression studies, where researchers try to identify the smallest possible set of ...
Ramón Díaz-Uriarte, Sara Alvarez de ...
BMCBI
2008
127views more  BMCBI 2008»
13 years 5 months ago
Gene and pathway identification with Lp penalized Bayesian logistic regression
Background: Identifying genes and pathways associated with diseases such as cancer has been a subject of considerable research in recent years in the area of bioinformatics and co...
Zhenqiu Liu, Ronald B. Gartenhaus, Ming Tan, Feng ...