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CSB
2005
IEEE
205views Bioinformatics» more  CSB 2005»
14 years 2 months ago
Fractal Clustering for Microarray Data Analysis
DNA microarray experiments generate a substantial amount of information about global gene expression. Gene expression profiles can be represented as points in multi-dimensional sp...
Lu-Yong Wang, Ammaiappan Balasubramanian, Amit Cha...
BMCBI
2007
133views more  BMCBI 2007»
13 years 8 months ago
Semi-supervised learning for the identification of syn-expressed genes from fused microarray and in situ image data
Background: Gene expression measurements during the development of the fly Drosophila melanogaster are routinely used to find functional modules of temporally co-expressed genes. ...
Ivan G. Costa, Roland Krause, Lennart Opitz, Alexa...
ISBRA
2009
Springer
14 years 3 months ago
Practical Quality Assessment of Microarray Data by Simulation of Differential Gene Expression
There are many methods for assessing the quality of microarray data, but little guidance regarding what to do when defective data is identified. Depending on the scientific questio...
Brian E. Howard, Beate Sick, Steffen Heber
BMCBI
2008
160views more  BMCBI 2008»
13 years 8 months ago
A comparison of four clustering methods for brain expression microarray data
Background: DNA microarrays, which determine the expression levels of tens of thousands of genes from a sample, are an important research tool. However, the volume of data they pr...
Alexander L. Richards, Peter Holmans, Michael C. O...
BMCBI
2011
13 years 5 days ago
Gene set analysis for longitudinal gene expression data
Background: Gene set analysis (GSA) has become a successful tool to interpret gene expression profiles in terms of biological functions, molecular pathways, or genomic locations. ...
Ke Zhang, Haiyan Wang, Arne C. Bathke, Solomon W. ...