svmGO {fgga} | R Documentation

## GO-term predictions by binary SVM classifiers

### Description

svmGO delivers soft GO-term predictions based on binary SVM classification models.

### Usage

    svmGO(svmMoldel, dxCharacterized, rootNode, varianceSVM)

### Arguments

`svmMoldel` | A list of object of class “svm" created by svm.
`dxCharacterized` | A data frame with ‘n’ protein coding genes (rows) by ‘f’ features (columns).
`rootNode` | A character indicating the root of the graph.
`varianceSVM` | A vector named with the variance of GO node labels.

### Details

Binary SVM predictions are supplemented with their corresponding margins. These margins are used to model the additive zero-mean Gaussian learning noise that corrupts ideal but hidden GO-term predictions. These ideal predictions are embedded in hidden variable nodes of the Forney Factor Graph.

### Value

`svmGO` | A named vector of predicted values for a protein sequence.

### Author(s)

Flavio E. Spetale, Pilar Bulacio and Javier Murillo <spetale@cifasis-conicet.gov.ar>

### References

Chang, Chih-Chung and Lin, Chih-Jen: LIBSVM: a library for Support Vector Machines http://www.csie.ntu.edu.tw/~cjlin/libsvm

Eisner R, Poulin B, Szafron D, Lu P, Greiner R. Improving protein function prediction using the hierarchical structure of the Gene Ontology. In: Proc. IEEE CIBCB; 2005. p. 1–1

Spetale FE, Tapia E, Krsticevic F, Roda F, Bulacio P (2016). A Factor Graph Approach to Automated GO Annotation. PLOS ONE 11(1): e0146986

### See Also

`svmTrain`

### Examples

    data(CfData)

    mygraphGO <- as(CfData[["graphCfGO"]], "graphNEL")

    modelSVMs <- lapply(CfData[["nodesGO"]][1:4], FUN = svmTrain,
                        tableGOs = CfData[["tableCfGO"]],
                        dxCharacterized = CfData[["dxCf"]],
                        graphGO = mygraphGO, kernelSVM = "radial")

    rootGO <- leaves(mygraphGO, "in")

    varianceGOs <- CfData[["varianceGOs"]]

    # SVM testing in four GO-terms
    dxTestCharacterized <- CfData[["dxCf"]][
                                    sample(1:dim(CfData[["dxCf"]])[1], 20), ]

    matrixGOTest <- svmGO(svmMoldel = modelSVMs,
                            dxCharacterized = dxTestCharacterized,
                            rootNode = rootGO, varianceSVM = varianceGOs)

[Package fgga version 1.2.0 Index]