Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma.

By G. Gordon, R. Jensen, L. Hsiao, S. Gullans, Joshua Blumenstock, Sridhar Ramaswamy, W. Richards, D. Sugarbaker, R. Bueno. Cancer Research, 2002

931 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/23e1ac88dec4985ecb9fa04867a2bfd524166fa3

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Citations in the corpus

A balanced iterative random forest for gene selection from microarray data (2013, methodology): “• Lung cancer dataset is also used in the experiments and it was generated with an Affymetrix oligonucleotide microarrays and normalized by z-score [18].”
Linear discriminant analysis for the small sample size problem: an overview (2014, unknown): “Lung (Gordon et al., 2002) Contains gene expression levels of malignant mesothelioma (MPM) and adenocarcinoma (ADCA) of the lung.”
iArc monogrAphs on the evAluAtion oF cArcinogenic risks to humAns (2012, background): “Receptors expressed by macrophages and other target cells in the lung that bind mineral fibres include MARCO, a scavenger receptor class A, and integrin receptors (Boylan et al., 1995; Gordon et al., 2002; Arredouani et al., 2005).”
Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection (2016, methodology): “2012b) [98] Null LDA technique & SRBCT [76]; NNC Sensitivity analysis; k-fold CV & Lung [68]; & Leukemia [66], [67];”
Circulating microRNAs in plasma of patients with gastric cancers (2010, methodology): “Determining the expression ratios of genes or miRNAs has been reported to be a useful technique to improve diagnostic potential (Gordon et al, 2002; Avissar et al, 2009).”
Biclustering in data mining (2008, background): “…point out: • ALL vs. AML data set [51,52]; • HuGE (Human GEnome) data set [53,54]; • Colon Cancer data set [55]; • B-cell lymphoma data set [56,57]; • Yeast Microarray data set [58,59]; • Lung Cancer data set [60]; • MLL Leukemia data set [61].”
A review of microarray datasets and applied feature selection methods (2014, unknown): “Lung 181 12,533 83–17% [49] 2002 [24,25,27,48] [8,2] Lung 410 2428 34–66% [103] 2008 [31] unknown”
Histology image analysis for carcinoma detection and grading (2012, background): “To address these issues, more fundamental and accurate knowledge at the molecular level, such as gene expression profiles [7,10,54] or proteomic signatures [132], are usually applied for a finer, hierarchical classification.”
Clustering cancer gene expression data: a comparative study (2008, unknown): “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 ”
Ensemble machine learning on gene expression data for cancer classification. (2003, unknown): “Lung cancer (Gordon et al 2002).”
Simple decision rules for classifying human cancers from gene expression profiles (2005, unknown): “, 2002) Lung Affy 12,533 150 (A) 31 (M) (Gordon et al., 2002) Prostate1 Affy 12,600 52 (T) 50 (N) (Singh et al.”
Reliability and reproducibility issues in DNA microarray measurements. (2006, background): “Studies based on expression profiling of human cancer samples with various clinical outcomes usually produce gene-expression classifiers involving ten to 100 genes [4,5,57–59], although microarray-based classifiers with as few as two genes have also been published [ 60 ,61].”