Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data

By Baolin Wu, Tom Abbott, David A. Fishman, Walter McMurray, Gil Mor, Kathryn L. Stone, David Ward, Kenneth R. Williams, Hongyu Zhao. Bioinform., 2003

477 citations recorded by the source corpus.

Subfields: Oncology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/504ccd6a13134f4d8ef417e8dd8e3541959c14b6

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Citations in the corpus

Fast Discrimination of Apple Varieties Using Vis/NIR Spectroscopy (2007, methodology): “Wu et al.([18]) compared several methods for classification based on mass spectra, including linear and quadratic discriminant analysis and classification trees methods.”
A Review of Ensemble Methods in Bioinformatics (2010, methodology): “A thorough comparison study was conducted by Wu et al. [36], who compared the ensemble methods of bagging, boosting, and random forests to individual classifiers of LDA, quadratic discriminant analysis, kNN, and SVM for MALDI-TOF (matrix assisted laser desorption/ionization with time-of-flight)…”
A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification (2008, background): “In the last few years substantial interest has developed within the bioinformatics community in the random forest algorithm [2] for classification of microarray and other high-dimensional molecular data [3-5].”
Gene selection and classification of microarray data using random forest (2006, unknown): “[25] and [ 20 ] use filtering approaches and, thus, do not take advantage of the measures of variable importance returned by random forest as part of the algorithm.”
Comparing protein abundance and mRNA expression levels on a genomic scale (2003, unknown): “In a recent study [ 16 ], which compared the relative ability of several different statistical approaches to classify samples based on MS data, the disease biomarker approach 117.2 Genome Biology 2003, Volume 4, Issue 9, Article 117 Greenbaum et al. http://genomebiology.com/2003/4/9/117”
Gaussian process regression for multivariate spectroscopic calibration (2007, unknown): “the classification of ovarian cancer using mass spectrometry [1]; and detection problems, e.”
NONCONCAVE PENALIZED M-ESTIMATION WITH A DIVERGING NUMBER OF PARAMETERS (2011, methodology): “Wu et al. (2003) investigated the classification of ovarian cancer by using several statistical methods, and Yu et al.”
Statistical and Computational Methods for Comparative Proteomic Profiling Using Liquid Chromatography-Tandem Mass Spectrometry (2005, methodology): “Wu et al. (47) compared the performance of LDA, QDA, k -nearest neighbor ( k (cid:4) 1–3, Euclidean metric), bagging and boosting classification trees, SVM (kernel not specified), and RF on MALDI data, using both a t -test rank and the by-product of the RF algorithm for m/z feature selection (15…”