Knowledge-based analysis of microarray gene expression data by using support vector machines.

By Michael S. Brown, William Noble Grundy, D. Lin, Nello Cristianini, Charles W. Sugnet, Terrence S. Furey, M. Ares, David Haussler. Proceedings of the National Academy of Sciences of the United States of America, 2000

2,522 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a86171e13f84fe32212dd7fb6a1c31a34a47155f

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Citations in the corpus

Prediction of Protein Function Using Protein–Protein Interaction Data (2002, methodology): “Clustering analysis of gene expression data can be used to predict functions of unannotated proteins based on the idea that genes with similar functions are likely to be co-expressed [3, 7, 23].”
Accurate Cancer Classification Using Expressions of Very Few Genes (2007, methodology): “In fact, SVMs have already been used in gene expression data analysis [7], [17].”
Comparing naive Bayes, decision trees, and SVM with AUC and accuracy (2003, result): “Our results on SVM may be inconsistent with some other comparisons involving SVM which showed superiority of SVM over other learning algorithms [17, 13, 3 ]. We think that one major difference is data pre-processing: we have discretized all numerical attributes (see Section 2.1) as Naive Bayes requ”
Using AUC and accuracy in evaluating learning algorithms (2005, methodology): “SVM over other learning algorithms [25], [19], [ 4 ], [16] We think that one major difference is”
Artificial Intelligence in Precision Cardiovascular Medicine. (2017, methodology): “In addition, an SVM is feasible for nonlinear data or large and complex data, such as “omic” data, because kernel functions, a shortcut to expedite learning process, can be applied to SVMs to enhance their accuracy and reduce processing time (10,11).”
Fuzzy rule extraction from support vector machines (2005, background): “In recent years, this learning strategy has been applied successfully in a wide variety of applications in classification and regression [7], [8], [9], [10], [11].”
Penalized Model-Based Clustering with Application to Variable Selection (2007, background): “.3 Gene function discovery using gene expression profiles Because many genes still have unknown functions, a biologically important subject is to computationally predict gene functions using, e.g., gene expression profiles (Brown et al., 2000).”
Improved prediction of protein-protein binding sites using a support vector machines approach. (2005, background): “Molecular biology applications have included geneexpression classification (Brown et al., 2000), protein classification (Zavaljevski et al., 2002; Dobson and Doig, 2003), protein fold recognition (Ding and Dunchak, 2001) and prediction of protein solvent accessibility (Yuan et al., 2002), β-edge…”
Feature Selection via Dependence Maximization (2012, neutral): “, 2000)(18), and yeast (Brown et al., 2000)(19).”
GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function (2008, background): “SVMs had previously been shown to be among the best algorithms for predicting gene function [27,28].”
Adaptive computation and machine learning (1998, methodology): “expression levels) of members within (positive examples) and outside (negative examples) the family [275, 95].”
Missing value estimation methods for DNA microarrays (2001, background): “as eigengenes (Alter et al., 2000; Anderson, 1984; Golub and Van Loan, 1996).”