fgga-package {fgga} | R Documentation

## FGGA: Factor Graph Gene ontology Annotation.

### Description

FGGA is a graph-based machine learning approach for the automated and consistent GO annotation of protein coding genes. The input is a set of GO-term annotated protein coding genes previously characterized in terms of a fixed number of user-defined features, including the presence/absence of PFAM domains, physical-chemical properties, presence of signal peptides, among others. The set of GO-terms defines the output GO subgraph. A hierarchical ensemble (SVMs) machine learning model is generated. This model can be used to predict the GO subgraph annotations of uncharacterized protein coding genes. Individual GO-term annotations are accompanied by maximum a posteriori probability estimates issued by the native message passing algorithm of factor graphs.

### Author(s)

Flavio E. Spetale, Javier Murillo and Elizabeth Tapia

BioInformatics

Cifasis-Conicet

spetale@cifasis-conicet.gov.ar

Maintainer: Flavio E. Spetale

### References

Spetale F.E., et al. A Factor Graph Approach to Automated GO Annotation. PLoS ONE (2016). https://doi.org/10.1371/journal.pone.0146986.

Spetale Flavio E., et al. Consistent prediction of GO protein localization. Scientific Report (2018). https://doi.org/10.1038/s41598-018-26041-z.

### See Also

`fgga`, `fgga2bipartite`, `sumProduct`, `svm`

[Package fgga version 1.2.0 Index]