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BMCBI
2006
118views more  BMCBI 2006»
13 years 6 months ago
Microarray image analysis: background estimation using quantile and morphological filters
Background: In a microarray experiment the difference in expression between genes on the same slide is up to 103 fold or more. At low expression, even a small error in the estimat...
Anders Bengtsson, Henrik Bengtsson
RECOMB
2004
Springer
14 years 7 months ago
Learning Regulatory Network Models that Represent Regulator States and Roles
Abstract. We present an approach to inferring probabilistic models of generegulatory networks that is intended to provide a more mechanistic representation of transcriptional regul...
Keith Noto, Mark Craven
BMCBI
2008
138views more  BMCBI 2008»
13 years 6 months ago
Combining transcriptional datasets using the generalized singular value decomposition
Background: Both microarrays and quantitative real-time PCR are convenient tools for studying the transcriptional levels of genes. The former is preferable for large scale studies...
Andreas W. Schreiber, Neil J. Shirley, Rachel A. B...
BMCBI
2010
125views more  BMCBI 2010»
13 years 6 months ago
Asymmetric microarray data produces gene lists highly predictive of research literature on multiple cancer types
Background: Much of the public access cancer microarray data is asymmetric, belonging to datasets containing no samples from normal tissue. Asymmetric data cannot be used in stand...
Noor B. Dawany, Aydin Tozeren
BMCBI
2004
180views more  BMCBI 2004»
13 years 6 months ago
Noise filtering and nonparametric analysis of microarray data underscores discriminating markers of oral, prostate, lung, ovaria
Background: A major goal of cancer research is to identify discrete biomarkers that specifically characterize a given malignancy. These markers are useful in diagnosis, may identi...
Virginie M. Aris, Michael J. Cody, Jeff Cheng, Jam...