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» Gene Classification using Expression Profiles: A Feasibility...
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BMCBI
2002
89views more  BMCBI 2002»
13 years 7 months ago
Sources of variability and effect of experimental approach on expression profiling data interpretation
Background: We provide a systematic study of the sources of variability in expression profiling data using 56 RNAs isolated from human muscle biopsies (34 Affymetrix MuscleChip ar...
Marina Bakay, Yi-Wen Chen, Rehannah H. A. Borup, P...
BMCBI
2007
169views more  BMCBI 2007»
13 years 7 months ago
Transcription factor target prediction using multiple short expression time series from Arabidopsis thaliana
Background: The central role of transcription factors (TFs) in higher eukaryotes has led to much interest in deciphering transcriptional regulatory interactions. Even in the best ...
Henning Redestig, Daniel Weicht, Joachim Selbig, M...
BMCBI
2006
129views more  BMCBI 2006»
13 years 7 months ago
Identifying genes that contribute most to good classification in microarrays
Background: The goal of most microarray studies is either the identification of genes that are most differentially expressed or the creation of a good classification rule. The dis...
Stuart G. Baker, Barnett S. Kramer
BMCBI
2006
198views more  BMCBI 2006»
13 years 7 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
169views more  BMCBI 2008»
13 years 7 months ago
A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification
Background: Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular sig...
Alexander R. Statnikov, Lily Wang, Constantin F. A...