Analysis of large-scale gene expression data.

By Gavin Sherlock. Current Opinion in Immunology, 2000

434 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3afca714a3ea0dfae9ce03b04461b17f0cd1911f

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Citations in the corpus

A new approach to decoding life: systems biology. (2001, methodology): “Methods for analyzing expression data, including gene-expression clustering and its extensions, have been extensively reviewed elsewhere (21, 30, 64, 108, 141).”
Promoter features related to tissue specificity as measured by Shannon entropy (2005, methodology): “The tree in Figure 3 was built by sampling 5,000 sets of 1,000 probe sets and clustering tissues using Pearson correlation and a centered measure using the XCLUSTER [55] program.”
A Gibbs Sampling Method to Detect Overrepresented Motifs in the Upstream Regions of Coexpressed Genes (2001, positive): “Such large-scale microarray experiments have opened new research directions in transcript pro (cid:142) ling (Sherlock, 2000; Szallasi, 1999; Wen et al. , 1998; Zhang, 1999).”
The Stanford Microarray Database (2001, background): “Self-Organizing Maps (7,8), which use XCluster underneath ( 9 ) (http://genome-www.stanford.edu/~sherlock/cluster.html).”
Multiple common variants for celiac disease influencing immune gene expression (2010, positive): “The first principal component (PC), explaining ∼80–90% of the total varianc”
Java Treeview - extensible visualization of microarray data (2004, methodology): “Combined with an analysis program, such as XCluster (Sherlock, 2000) (http://genomewww.stanford.edu/∼sherlock/cluster .html) or Cluster 3.0 (De Hoon et al., 2004), a spreadsheet editor such as Excel, and scripted automation using packages such as PCL Analyisis (http://pcl-analysis.sourceforge.net),…”
Cluster analysis for gene expression data: a survey (2004, background): “Second, gene expression data typically contain a huge amount of noise; however, the K-means algorithm forces each gene into a cluster, which may cause the algorithm to be sensitive to noise [59], [57].”