svmGO {fgga} | R Documentation |
svmGO delivers soft GO-term predictions based on binary SVM classification models.
svmGO(svmMoldel, dxCharacterized, rootNode, varianceSVM)
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. |
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.
svmGO |
A named vector of predicted values for a protein sequence. |
Flavio E. Spetale, Pilar Bulacio and Javier Murillo <spetale@cifasis-conicet.gov.ar>
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
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)