An extensive comparison of recent classification tools applied to microarray data

By Jae Won Lee, Jung Bok Lee, Mira Park, Seuck Heun Song. Computational Statistics & Data Analysis, 2004

453 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c457e9005619f11f3681dc6b90bc3708df0ba326

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Citations in the corpus

A Review of Ensemble Methods in Bioinformatics (2010, unknown): “This is demonstrated by several studies such as [39] and [40].”
A framework to identify physiological responses in microarray-based gene expression studies: selection and interpretation of biologically relevant genes. (2008, background): “In microarray studies, RF was shown to outperform other classification methods, especially when the number of classes is moderate (13, 25).”
Data mining for credit card fraud: A comparative study (2011, background): “Other studies comparing the performance of different learning algorithms over multiple datasets have found random forest to show good overall performance [9,10,27,31].”
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].”
A review of feature selection techniques in bioinformatics (2007, background): “The prevalence of these univariate techniques has dominated the field, and up to now comparative evaluations of different classification and FS techniques over DNA microarray datasets only focused on the univariate case [29, 64, 72, 113].”
Applications of functional data analysis: A systematic review (2013, methodology): “The LDA and QDA are both classical clustering methods and commonly used in microarray analysis [124,125].”
A review of microarray datasets and applied feature selection methods (2014, background): “The univariate paradigm, which is fast and scalable but which ignores feature dependencies, has dominated the field during the 2000s [36,74,72].”
Random Forests for Genomic Data Analysis (2012, background): “[24] presented a comprehensive comparison of RF to LDA, QDA, logistic”
CuMiDa: An Extensively Curated Microarray Database for Benchmarking and Testing of Machine Learning Approaches in Cancer Research (2019, unknown): “…to the data sets available in CuMiDa are in agreement with the scientiﬁc literature, where SVM and RF displayed the overall higher accuracy (Lee et al., 2005; Pirooznia et al., 2008; Statnikov et al., 2008) (Supplementary Table S1), exhibiting a mean of 88% and 85% of classiﬁcation…”