Prediction of central nervous system embryonal tumour outcome based on gene expression

By S. Pomeroy, Pablo Tamayo, M. Gaasenbeek, Lisa-Marie Sturla, Michael Angelo, M. Mclaughlin, John Y. H. Kim, L. Goumnerova, P. Black, C. Lau and 15 more. Nature, 2002

2,465 citations recorded by the source corpus.

Subfields: Molecular Biology, Pathology and Forensic Medicine

View Paper: https://www.semanticscholar.org/paper/1d9dece252de9457f504c8e79efe50fda73a2199

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Citations in the corpus

Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis (2007, methodology): “Each sub-problem is solved by a fast non-negativity constrained least squares (NLS) algorithm (van Benthem and Keenan, 2004) that is improved upon the active set based NLS method. Bro and de Jong (1997) made a substantial speed improvement to Lawson and Hanson’s algorithm (Lawson and Hqnson, 1974) f”
Simple decision rules for classifying human cancers from gene expression profiles (2005, neutral): “, 1999) CNS Affy 7,129 25 (C) 9 (D) (Pomeroy et al., 2002) DLBCL Affy 7,129 58 (D) 19 (F) (Shipp et al.”
A Bayesian missing value estimation method for gene expression profile data (2003, background): “Gene expression profile analyses were also effective in cancer prognosis prediction, even when morphological or immunohistological study was difficult (Alizadeh et al., 2000; Kihara et al., 2001; Pomeroy et al., 2002; Shipp et al., 2002; van’t Veer et al., 2002).”
Gene expression-based classification of malignant gliomas correlates better with survival than histological classification. (2003, methodology): “Paradoxically, ribosomal protein-encoding genes were found recently to be correlated with poor outcome in medulloblastoma (16).”
Cancer stem cells in nervous system tumors (2004, background): “Microarray analysis of human medulloblastomas suggests a similarity of gene expression with normal developing brain cells (Pomeroy et al., 2002).”
Clustering cancer gene expression data: a comparative study (2008, neutral): “Armstrong-V1 [52] Affy Blood 72 2 24,48 12582 1081 Armstrong-V2 [52] Affy Blood 72 3 24,20,28 12582 2194 Bhattacharjee [9] Affy Lung 203 5 139,17,6,21,20 12600 1543 Chowdary [13] Affy Breast, Colon 104 2 62,42 22283 182 Dyrskjot [14] Affy Bladder 40 3 9,20,11 7129 1203 Golub-V1 [3] Affy Bone marrow ”
ONCOMINE: a cancer microarray database and integrated data-mining platform. (2004, neutral): “potential, and patient survival [1–24].”
Estimating Dataset Size Requirements for Classifying DNA Microarray Data (2003, positive): “…have statistical and clinical relevance for a variety of tumor types: leukemia (Golub et al. , 1999), lymphoma (Shipp et al. , 2001), brain cancer (Pomeroy et al. , 2002), lung cancer (Bhattacharjee et al. , 2001) and the classi(cid:142) cation of multiple primary tumors (Ramaswamy et al. , 2001,…”
Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems (2011, background): “The Brain data set compares 5 embryonal tumors [45] with 5597 gene expression.”
Ensemble machine learning on gene expression data for cancer classification. (2003, background): “Central nervous system (CNS) embryonal tumour outcome (Pomeroy et al 2002).”
Coexpression analysis of human genes across many microarray data sets. (2004, neutral): “…et al. 1999) 26 3027 42105 (Armstrong et al. 2002) 72 8242 213456 (Perou et al. 2000) 84 5701 167826 (Bhattacharjee et al. 2001) 203 8243 243303 (Pomeroy et al. 2002) 90 5418 85909 (Bittner et al. 2000) 38 4382 16141 (Ramaswamy et al. 2001) 255 9528 372500 (Butte et al. 2000) 68 4906 81755…”
Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma. (2002, methodology): “In this analysis, we used previously published microarray data ( 6 ) for a set of 60 medulloblastoma tumors with linked clinical data (Dataset “C” )t o create a ratio-based test designed to predict patient outcome after treatment.”