Molecular classification of human carcinomas by use of gene expression signatures.

By Andrew I. Su, J. Welsh, L. Sapinoso, S. Kern, P. Dimitrov, H. Lapp, Peter G Schultz, S. Powell, C. Moskaluk, H. Frierson and 1 more. Cancer Research, 2001

693 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4a64aeb93ec41061e063da3f764e57ebbadd3e0a

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Citations in the corpus

MYB function in normal and cancer cells (2008, methodology): “These data have been reproduced in several mRNA profiling studie”
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 ”
Feature selection for high-dimensional class-imbalanced data sets using Support Vector Machines (2014, background): “prostate, bladder/ureter, breast, colorectal, gastroesophagus, kidney, liver, ovary, pancreas, lung adenocarcinomas and lung squamous cell carcinoma [36, 47].”
From patterns to pathways: gene expression data analysis comes of age (2002, background): “It is, however, possible to combine binary predictors together to separate multiple classe”
RNA-Seq of Tumor-Educated Platelets Enables Blood-Based Pan-Cancer, Multiclass, and Molecular Pathway Cancer Diagnostics (2015, unknown): “Next, we determined the diagnostic accuracy of TEP-based pan-cancer classification in the training cohort (n = 175), employing a leave-one-out cross-validation support vector machine algorithm (SVM/LOOCV, see Experimental Procedures; Figures S1D andS1E), previously used to classify primary andmetast”
Efficient and Robust Feature Selection via Joint ℓ2, 1-Norms Minimization (2010, methodology): “In our experiments, we used five publicly available microarray data sets and one Mass Spectrometry (MS) data sets: ALLAML data set [6], the malignant glioma (GLIOMA) data set [17], the human lung carcinomas (LUNG) data set [2], Human Carcinomas (Carcinomas) data set [24, 27], Prostate Cancer gene ex”
Oncomine 3.0: genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles. (2007, background): “multicancer data set [25], and representative enriched molecular concepts are presented.”
Simple decision rules for classifying human cancers from gene expression profiles (2005, unknown): “, 2002) Cancers Affy 11 12,533 100 74 (Su et al., 2001) GCM Affy 14 16,063 144 46 (Ramaswamy et al.”
Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. (2002, background): “However, within the TEL-AML1 ferences between either lung adenocarcinoma (Su et al., 2001) or melanoma (Ramaswamy et al., 2001) and bladder transitional subgroup, a distinct expression signature consisting of 20 genes was defined that identified, with 100% accuracy in crossvalidacarcinoma , tumors…”
A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis (2004, unknown): “The preliminary experimental evidence currently available suggests that some multicategory SVMs (MC-SVMs) perform well in isolated gene expression-based cancer diagnostic experiments ([Yeo2001], [ Su2001 ], [Ramaswamy2001], [Yeang2001], and [Lee2003]).”
Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection (2016, methodology): “PFA) & Multi-tissue [72], [85]; Linear SVM Stability analysis: Kuncheva”
Probabilistic model of the human protein-protein interaction network (2005, methodology): “Five data sets were selected for the final analysis: a multi-cancer data set profiling of 174 cancer samples of 11 tissue types 42 , a breast cancer data set profiling 117 breast tumors 43 , a liver data set profiling 197 normal and cancerous liver samples 44 , a lymphoma data set profiling 293…”
Revisiting Global Gene Expression Analysis (2012, unknown): “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., ”
Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions (2003, background): “…responsible for, or indicative of, the occurrence of different cancers (DeRisi et al., 1996; Golub et al., 1999; Bittner et al., 2000; Furey et al., 2000; Hedenfalk et al., 2001; Khan et al., 2001; Ramaswamy et al., 2001; Su et al., 2001; Yeang et al., 2001; Pomeroy et al., 2002) or other diseases.”