svmTrain: Binary SVM classification models for individual Ontology-term...

In fspetale/fgga: Hierarchical ensemble method based on factor graph

Binary SVM classification models for individual Ontology-term predictions

Description

svmTrain delivers a set of binary SVM classifiers for different Ontology-terms.

Usage

svmTrain(nodeGraph, tableOntoTerms, dxCharacterized, graphOnto,
        kernelSVM = "radial")

Arguments

nodeGraph	A character indicating a GO node label
tableOntoTerms	A binary matrix with ‘n’ proteins (rows) by ‘m’ Ontology node labels (columns).
dxCharacterized	A data frame with ‘n’ protein coding genes (rows) by ‘f’ features (columns).
graphOnto	A graphNEL graph with ‘m’ Ontology node labels.
kernelSVM	The kernel used to calculate the variance (default: radial).

Details

Starting from sets of positively annotated protein sequences to different GO-terms in a GO subgraph, corresponding sets of negatively annotated protein sequences are computed using the inclusive separation policy proposed by Eisner et al. Training datasets for each GO-term are used to train binary Support Vector Machine (SVM) classifiers with a variety of kernel options.

Value

svmTrain

A list of objects of “svm" class containing the fitted model.

Author(s)

Flavio E. Spetale, Pilar Bulacio and Javier Murillo <[e-mail]>

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

svmOnto

Examples

data(CfData)

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

# SVM training in four GO-terms
modelSVMs <- lapply(CfData[["nodesGO"]][1:4], FUN = svmTrain,
                    tableOntoTerms = CfData[["tableCfGO"]],
                    dxCharacterized = CfData[["dxCf"]],
                    graphOnto = mygraphGO, kernelSVM = "radial")

fspetale/fgga documentation built on Jan. 29, 2024, 6:53 p.m.