Multiclass cancer diagnosis using tumor gene expression signatures

By Sridhar Ramaswamy, Pablo Tamayo, Ryan M. Rifkin, Sayan Mukherjee, Chen-Hsiang Yeang, Michael Angelo, C. Ladd, Michael Reich, É. Latulippe, Jill P. Mesirov and 5 more. Proceedings of the National Academy of Sciences of the United States of America, 2001

2,146 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e1890fe1b67a6e47b6b21016870f0fce20960ea3

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Citations in the corpus

Class Prediction by Nearest Shrunken Centroids, with Applications to DNA Microarrays (2003, methodology): “We also tried both support vector machines (Ramaswamy et al., 2001) and regularized discriminant analysis (Section 7).”
The Entire Regularization Path for the Support Vector Machine (2004, methodology): “3 Microarray classification We illustrate our algorithm on a large cancer expression data set [Ramaswamy et al., 2001].”
A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression (2004, background): “The GCM dataset (Ramaswamy et al., 2001; Yeang et al., 2001) consists of 198 human tumor samples of 15 types.”
Relation Between Permutation-Test P Values and Classifier Error Estimates (2003, methodology): “We note that SVMs have been used for expression-based classification (Yeang et al., 2001; Ramaswamy et al., 2001).”
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.”
Using deep learning to enhance cancer diagnosis and classication (2013, methodology): “Ramaswamy et al. in (Ramaswamy et al., 2001) applied recursive feature elimination us-ing SVM to ﬁnd similarly a small number of gene expressions to be used as the feature space for the classiﬁcation.”
Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. (2002, background): “, 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 crossvalida- carcinoma, tumors that conceptually would be considered more divergent.”
Foundations of Rule Learning (2012, methodology): “In our rule induction experiments a publicly available dataset described by Ramaswamy et al. (2001) was used.”
Clustering cancer gene expression data: a comparative study (2008, background): “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 ”
Linear discriminant analysis for the small sample size problem: an overview (2014, neutral): “Images of human heads with views ranging from frontal to left and right profiles. http://www.itl.nist.gov/iad/humanid/feret/feret_master.html Yale (Belhumeur et al. 1997 GCM (Ramaswamy et al., 2001) This Global Cancer Map (GCM) dataset has 14 classes with 144 training samples and 46 testing samples.”
Classification of Gene Expression Data by Majority Voting Genetic Programming Classifier (2006, methodology): “In addition to clustering, different machine learning classifiers like support vector machine (SVM) [9], k-nearest neighbor classifier [10], etc have been applied to the classification of gene expression data [7] [8] [11] [12] [13] [14].”
Gene selection and classification of microarray data using random forest (2006, neutral): “• Support Vector Machines (SVM) SVM are becoming increasingly popular classifiers in many areas, including microarrays [53-55].”
Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems (2011, result): “The Multiple Tumor data set initially compared 14 tumors [46] and 7129 gene expressions.”