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ICML
2004
IEEE
14 years 8 months ago
K-means clustering via principal component analysis
Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means clustering is a commonly used data clustering for unsupervi...
Chris H. Q. Ding, Xiaofeng He
BMCBI
2007
105views more  BMCBI 2007»
13 years 7 months ago
Extended analysis of benchmark datasets for Agilent two-color microarrays
Background: As part of its broad and ambitious mission, the MicroArray Quality Control (MAQC) project reported the results of experiments using External RNA Controls (ERCs) on fiv...
Kathleen F. Kerr
BMCBI
2007
154views more  BMCBI 2007»
13 years 7 months ago
Classification of heterogeneous microarray data by maximum entropy kernel
Background: There is a large amount of microarray data accumulating in public databases, providing various data waiting to be analyzed jointly. Powerful kernel-based methods are c...
Wataru Fujibuchi, Tsuyoshi Kato
MEMICS
2010
13 years 2 months ago
Modeling Gene Networks using Fuzzy Logic
Recently, almost uncontrolled technological progress allows so called high-throughput data collection for sophisticated and complex experimental biological systems analysis. Espec...
Artur Gintrowski
BMCBI
2006
151views more  BMCBI 2006»
13 years 7 months ago
Modeling Sage data with a truncated gamma-Poisson model
Background: Serial Analysis of Gene Expressions (SAGE) produces gene expression measurements on a discrete scale, due to the finite number of molecules in the sample. This means t...
Helene H. Thygesen, Aeilko H. Zwinderman