Multiple SVM-RFE for gene selection in cancer classification with expression data
By Kai-Bo Duan, Jagath Rajapakse, Haiying Wang, Francisco Javier Azuaje. IEEE Transactions on Nanobioscience, 2005
452 citations recorded by the source corpus.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/7294cc55a3b09a43cde606085b1a5742277b9e09
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Molecular Biology
- David I. Ellis
- Royston Goodacre
- Yun Xu
- Howbeer Muhamadali
- Elon Santos Correa
- Michael L. Turner
- P. Gromski
- Piyushkumar A. Mundra
Citations in the corpus
- Semantic Similarity in Biomedical Ontologies (2009, background): “However, it can also be used to link and compare results from different assays [30], to improve data quality [70], and to validate gene selection for biomedical purposes [71].”
- A balanced iterative random forest for gene selection from microarray data (2013, methodology): “The accuracy of SVM-RFE, MSVM-RFE, RF and NB is 83.71% [20], 83.57% [20], 87% [7] and 87% [25], respectively.”
- A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue (2007, methodology): “Some approaches, including SVMbased recursive feature elimination (SVM-RFE) [19] and its improvements [35] are proposed to deal with above issue.”
- SVM-RFE With MRMR Filter for Gene Selection (2010, methodology): “GO helps to evaluate functional similarities among a set of genes [22], [29], as there exists a direct relationship”
- A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding. (2015, methodology): “the classifier [113], orbyselection of relevant features via application of SVM-based on recursive feature elimination (RFE) [114,115].”
- Feature selection methods for big data bioinformatics: A survey from the search perspective. (2016, methodology): “[70] carried out a statistical analysis of weight vectors of multiple linear SVMs trained on subsamples of the original training data, as opposed to one such SVM in SVM-RFE.”