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This is the development version of SVMDO; for the stable release version, see SVMDO.

Identification of Tumor-Discriminating mRNA Signatures via Support Vector Machines Supported by Disease Ontology

Bioconductor version: Development (3.20)

It is an easy-to-use GUI using disease information for detecting tumor/normal sample discriminating gene sets from differentially expressed genes. Our approach is based on an iterative algorithm filtering genes with disease ontology enrichment analysis and wilk and wilk’s lambda criterion connected to SVM classification model construction. Along with gene set extraction, SVMDO also provides individual prognostic marker detection. The algorithm is designed for FPKM and RPKM normalized RNA-Seq transcriptome datasets.

Author: Mustafa Erhan Ozer [aut, cre] , Pemra Ozbek Sarica [aut], Kazim Yalcin Arga [aut]

Maintainer: Mustafa Erhan Ozer <erhanozer19 at>

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biocViews Classification, DifferentialExpression, GUI, GeneSetEnrichment, RNASeq, Software, Survival, Transcriptomics
Version 1.5.0
In Bioconductor since BioC 3.17 (R-4.3) (1 year)
License GPL-3
Depends R (>= 4.3), shiny (>= 1.7.4)
Imports shinyFiles (>= 0.9.3), shinytitle (>= 0.1.0), golem (>= 0.3.5), nortest (>= 1.0-4), e1071 (>= 1.7-12), BSDA (>= 1.2.1), data.table (>= 1.14.6), sjmisc (>= 2.8.9), klaR (>= 1.7-1), caTools (>= 1.18.2), caret (>= 6.0-93), survival (>= 3.4-0), DOSE(>= 3.24.2), AnnotationDbi(>= 1.60.0),>= 3.16.0), dplyr (>= 1.0.10), SummarizedExperiment(>= 1.28.0), grDevices, graphics, stats, utils
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