voomDDA protocols

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description
voomDDA computational protocol

voomDDA specifications

Information


Unique identifier OMICS_22482
Name voomDDA
Interface Web user interface
Restrictions to use None
Programming languages R
Computer skills Basic
Version 1.5
Stability Stable
Maintained Yes

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Maintainers


  • person_outline Gokmen Zararsiz <>
  • person_outline Gokmen Zararsiz <>

Publication for voomDDA

voomDDA IN pipelines

 (2)
2017
PMCID: 5633036
PMID: 29018623
DOI: 10.7717/peerj.3890

[…] vector machines) and rf (random forests) algorithms are also considered due to their good performances in microarray based classification studies. implementation details of each algorithm, including voomdda classifiers are given in below:, plda1: the data are normalized using the deseq median ratio method. normalized count values are taken as input to the plda algorithm. a five-fold cross […]

2017
PMCID: 5633036
PMID: 29018623
DOI: 10.7717/peerj.3890

[…] data are converted into a continuous scale using this mean and variance relationship. since plda1 and nblda are count-based classifiers, the transformations are not applied for these classifiers. voomdda classifiers use the voom method inside the algorithm for transformation. a power transformation is applied for plda2 classifier. the rlog transformation is performed for other classifiers, […]

voomDDA institution(s)
Department of Biostatistics, Erciyes University, Kayseri, Turkey; Turcosa Analytics Solutions Ltd Co, Erciyes Teknopark, Kayseri, Turkey; Department of Biostatistics, Hacettepe University, Ankara, Turkey; European Molecular Biology Laboratory, Heidelberg, Germany; Department of Biostatistics, Trakya University, Edirne, Turkey; Department of Biology, Istanbul University, Istanbul, Turkey
voomDDA funding source(s)
Supported by the Research Fund of Erciyes University [TDK-2015-5468].

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