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RECOMB
2002
Springer
15 years 10 months ago
A new approach to analyzing gene expression time series data
We present algorithms for time-series gene expression analysis that permit the principled estimation of unobserved timepoints, clustering, and dataset alignment. Each expression p...
Ziv Bar-Joseph, Georg Gerber, David K. Gifford, To...
BMCBI
2006
155views more  BMCBI 2006»
14 years 9 months ago
AffyMiner: mining differentially expressed genes and biological knowledge in GeneChip microarray data
Background: DNA microarrays are a powerful tool for monitoring the expression of tens of thousands of genes simultaneously. With the advance of microarray technology, the challeng...
Guoqing Lu, The V. Nguyen, Yuannan Xia, Michael Fr...
BMCBI
2006
118views more  BMCBI 2006»
14 years 9 months ago
Identification of gene expression patterns using planned linear contrasts
Background: In gene networks, the timing of significant changes in the expression level of each gene may be the most critical information in time course expression profiles. With ...
Hao Li, Constance L. Wood, Yushu Liu, Thomas V. Ge...
BMCBI
2008
160views more  BMCBI 2008»
14 years 9 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
2008
114views more  BMCBI 2008»
14 years 9 months ago
Partial mixture model for tight clustering of gene expression time-course
Background: Tight clustering arose recently from a desire to obtain tighter and potentially more informative clusters in gene expression studies. Scattered genes with relatively l...
Yinyin Yuan, Chang-Tsun Li, Roland Wilson