Gene expression-based classification of malignant gliomas correlates better with survival than histological classification.

By C. Nutt, D. Mani, Rebecca A. Betensky, Pablo Tamayo, J. Cairncross, C. Ladd, U. Pohl, C. Hartmann, M. Mclaughlin, T. Batchelor and 5 more. Cancer Research, 2003

970 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5c5e795736703d9aa7147fedbb1d669ea5c989f0

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Feiping Nie
2. Dacheng Tao
3. Jundong Li
4. Jie Gui
5. Huan Liu
6. Jiliang Tang
7. Yi Yang
8. Charu C. Aggarwal

Citations in the corpus

• Bayesian model averaging: development of an improved multi-class, gene selection and classification tool for microarray data (2005, methodology): “2001), response to therapy (Shipp et al. 2002; van 't Veer et al. 2002; Nutt et al. 2003).”
• Gene Expression Profiling of Gliomas Strongly Predicts Survival (2004, methodology): “A validation dataset was obtained from an independent analysis of gene expression in a series of 50 glioblastoma and anaplastic oligodendroglioma tumors (17).”
• Reconstruction-based Unsupervised Feature Selection: An Embedded Approach (2017, background): “We choose 8 benchmark datasets of various types for evaluation, including three image datasets, i.e., object image dataset COIL201 [Nene et al., 1996], face image dataset ORL2, hand- 1http://www.cad.zju.edu.cn/home/dengcai/Data/MLData.html 2http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabas”
• Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications (2007, background): “The second test-bed contains six microarray datasets: 9-tumors [29], Brain-tumor2 [30], Leukemia-1 [2], prostate-tumors [31], DLBCL [32] and SRBCT [33].”
• Kernel Methods and Machine Learning (2014, background): “Six cancer datasets were considered [208]: carcinoma [107], colon [5], glioma [197], leukemia [82], prostate [249], and breast [279].”
• Classification of Gene Expression Data by Majority Voting Genetic Programming Classifier (2006, methodology): “One widely used technique in cancer research to predict the classes of unknown samples is clustering, in which patient samples with similar clinical records are grouped together, and it is used with or without gene selection [2] [3] [4] [5] [6] [7] [8].”
• An integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR and NF1 (2010, background): “…and gene expression profiling studies identifying gene signatures associated with EGFR overexpression, clinical features, and survival (Freije et al., 2004; Liang et al., 2005; Mischel et al., 2003; Murat et al., 2008; Nutt et al., 2003; Phillips et al., 2006; Shai et al., 2003; Tso et al., 2006).”
• Feature Selection Based on Structured Sparsity: A Comprehensive Study (2017, methodology): “We did our experiments on 11 data sets: one WebKB data set WebKB-WC,2 one image classification data set coil20 [153], two face data sets including AR and Umist,3 two UCI data sets4 including protein and vehicle, and five microarray data sets including CAR [154], LUNG [155], MLL [156], GLA [157], and”
• Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. (2006, background): “Importantly, recent expression profiling studies have revealed that molecular classification of gliomas can be of prognostic value (Freije et al., 2004; Nutt et al., 2003).”
• Comprehensive genomic characterization defines human glioblastoma genes and core pathways (2008, unknown): “Alfred Yung(11), Oliver Bogler(12), University of California San Francisco: Scott VandenBerg(9), Mitchel Berger(51), Michael Prados(51)”
• 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 ”
• Prediction by Supervised Principal Components (2006, background): “Botstein and Brown 2001, Khan et al. 2001, Ramaswamy et al. 2001, Nguyen and Rocke 2002a, Nguyen and Rocke 2002b, Shipp et al. 2002, van ’t Veer et al. 2002, van de Vijver et al. 2002, Tibshirani et al. 2001, Nutt et al. 2003).”
• A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis (2004, unknown): “As we mentioned, the choice of KNN, NN, and PNN classifiers as the baseline techniques was grounded on prior successful applications to gene expression based cancer diagnosis (e.g., [Khan2001], [Ramaswamy2001], [Pomeroy2002], [ Nutt2003 ], [Signh2002], and [Berrar2003]).”