Molecular classification of cutaneous malignant melanoma by gene expression profiling

By M. Bittner, P. S. Meltzer, Yidong Chen, Yong Jiang, E. Seftor, M. Hendrix, Michael D. Radmacher, R. Simon, Zohar Yakhini, A. Ben-Dor and 16 more. Nature, 2000

2,061 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Oncology

View Paper: https://www.semanticscholar.org/paper/07160a69d4c32c7cee628711cc42049320746c6f

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Citations in the corpus

Cluster validation techniques for genome expression data (2003, background): “In the genome expression domain it has provided the basis for novel clinical diagnostic and prognostic studies [ 3 ].”
Transcriptional gene expression profiles of colorectal adenoma, adenocarcinoma, and normal tissue examined by oligonucleotide arrays. (2001, background): “Others have used expression analysis to distinguish characteristic patterns of expression in malignant melanoma and breast cancer, which seem to correlate with phenotype (19, 20).”
Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns. (2001, methodology): “This MDS clustering was based on a WGA (16) that generated a set of 113 genes that showed significantly differentiated expression levels between the two tumor categories.”
Clinical biomarkers in drug discovery and development (2003, neutral): “gov/cancer_information/cancer_type/melanoma/">melanoma”
Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks (2001, background): “The samples were again randomly partitioned and the entire training process repeated (7).”
Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. (2003, neutral): “Examples of class discovery are the studies by Bittner et al. ( 3 ) that examined gene expression profiles for advanced melanomas and by Alizadeh et al. (4) that examined the gene expression profiles of patients with diffuse large B-cell lymphoma.”
Revisiting Global Gene Expression Analysis (2012, neutral): “What are the implications for classifying cell states in disease? Significant effort is being devoted to expression profiling cancer cells and these studies use standard normalization methods (Alizadeh et al., 2000; Beer et al., 2002; Berger et al., 2010; Bhattacharjee et al., 2001; Bittner et al., ”
Gene Expression Profiling in Uveal Melanoma Reveals Two Molecular Classes and Predicts Metastatic Death (2004, neutral): “(6) identified a gene expression cluster, largely in metastatic melanoma cell lines, that correlated with invasive behavior in vitro.”
Tumor classification by partial least squares using microarray gene expression data (2002, background): “These techniques have also helped to identify previously undetected subtypes of cancer (Golub et al., 1999; Alizadeh et al., 2000; Bittner et al., 2000; Perou et al., 2000).”
Computational cluster validation in post-genomic data analysis (2005, methodology): “The biases of internal measures are illustrated in Figure 7 , where the application of several internal measures on the Long data set is shown.”