A gene expression database for the molecular pharmacology of cancer

By U. Scherf, D. Ross, M. Waltham, Lawrence H. Smith, Jae K. Lee, Lorraine K. Tanabe, K. Kohn, W. Reinhold, T. Myers, Darren T. Andrews and 7 more. Nature Genetics, 2000

1,467 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/feae36b19d8f8566aefbb8b30e9fb55c1592f0a7

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Citations in the corpus

Survey of clustering algorithms (2005, background): “Most of the published data sets include usually less than 20 samples for each tumor type, but with as many as thousands of gene measures [80], [109], [238], [268].”
RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. (2008, background): “The ability of these arrays to simultaneously interrogate thousands of transcripts has led to important advances in a wide range of biological problems, including the identification of gene expression differences among diseased and healthy tissues, and new insights into developmental processes, phar”
Sparse canonical methods for biological data integration: application to a cross-platform study (2009, methodology): “As previously reported [12,28,29], ME cell lines (including MDA_N and Table 1: Correlations.”
ABCB5-mediated doxorubicin transport and chemoresistance in human malignant melanoma. (2005, methodology): “previously (34, 35), by using the RNeasy Mini kit (Qiagen, Valencia, CA),”
CellMiner: a relational database and query tool for the NCI-60 cancer cell lines (2009, methodology): “…resistance gene MDR1 [13], (ii) to identify possible molecular target relationships for the drug Aminoflavone [21], and (iii) to identify asparagines synthetase expression as a potential biomarker for use of the enzyme-drug L-asparaginase for treatment of ovarian or other solid tumors [12,22].”
Iterative signature algorithm for the analysis of large-scale gene expression data. (2002, background): “However, while standard tools, like clustering algorithms [7, 8, 9, 10, 11, 12, 13, 14] (see [15, 16] for reviews) and Singular Value Decomposition (SVD) [17,18], provide interesting results when applied to relatively small data sets, typically containing tens of experimental conditions and at most…”
Bayesian Sparse Group Selection (2016, methodology): “The drug–gene interaction was first studied by Scherf et al. (2000). In their study, the Pearson correlations were used to search for genes whose abundance level may influence the sensitivity of the drug. Here, we search for genes by fitting the full regression model with pathway information conside”