Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses

By Arindam K Bhattacharjee, William G. Richards, J. Staunton, Cheng Li, Stefano Monti, P. Vasa, C. Ladd, J. Beheshti, Raphael Bueno, Michael A. Gillette and 9 more. Proceedings of the National Academy of Sciences of the United States of America, 2001

2,652 citations recorded by the source corpus.

Subfields: Molecular Biology, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/5bd0932694baed396796896bec59ed3ab5343bf6

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Citations in the corpus

Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data (2003, methodology): “The class of adenocarcinomas is highly heterogeneous, and it is widely accepted that clinically relevant AD subtypes exist, although their molecular and clinical profile is not well established or understood (Bhattacharjee et al., 2001).”
Bayesian model averaging: development of an improved multi-class, gene selection and classification tool for microarray data (2005, neutral): “1999), different tumor types (Golub et al. 1999; Alizadeh et al. 2000; Ross et al. 2000; Bhattacharjee et al. 2001; Ramaswamy et al. 2001), response to therapy (Shipp et al.”
Gene-expression profiles predict survival of patients with lung adenocarcinoma (2002, positive): “Analyses of lung cancers using array technologies have identified subgroups of tumors that differ according to tumor type and histological subclasses and, to a lesser extent, survival among adenocarcinoma patient”
Inferring Pathway Activity toward Precise Disease Classification (2008, positive): “We obtained previously published mRNA expression datasets covering seven different disease classification scenarios: 24 expression profiles of HeLa cells after stimulation by Tumor Necrosis Factor (TNF) [26], expression profiles of 62 primary prostate tumors and 41 normal prostate specimen [27], exp”
Finding large average submatrices in high dimensional data (2009, methodology): “The CC biclustering algorithm of Cheng and Church (2000) searches for maximal-sized submatrices such that the sum of squared residuals from an two-way ANOVA fit falls below a given threshold.”
Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks (2006, background): “Several studies have been conducted using microarray technology studying several types of cancer (Golub et al. (1999); Bhattacharjee et al. (2001); Singh et al. (2002); van't Veer et al. (2002); van de Vijver et al. (2002); Spentzos et al. (2004, 2005)).”
Statistical Significance of Clustering for High-Dimension, Low–Sample Size Data (2008, background): “These samples have been described in detail previously (Bhattacharjee et al. 2001).”