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.
INSTALLATION
The fgga R source package can be directly downloaded from Bioconductor repository or GitHub repository. This R package contains a experimental dataset as example, one pre-run R object and all functions needed to run FGGA.
## From Bioconductor repository
if (!requireNamespace(“BiocManager”, quietly = TRUE)) {
install.packages(“BiocManager”)}
BiocManager::install(“fgga”)
## Or from GitHub repository using devtools
BiocManager::install(“devtools”)
devtools::install_github(“fspetale/fgga”)
REFERENCES
1: Spetale F.E., Tapia E., Krsticevic F., Roda F. and Bulacio P. “A Factor Graph Approach to Automated GO Annotation”. PLoS ONE 11(1): e0146986, 2016.
2: Spetale Flavio E., Arce D., Krsticevic F., Bulacio P. and Tapia E. “Consistent prediction of GO protein localization”. Scientific Report 7787(8), 2018
fgga
R package
FGGA: Factor Graph Gene ontology Annotation
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.
INSTALLATION
The fgga R source package can be directly downloaded from Bioconductor repository or GitHub repository. This R package contains a experimental dataset as example, one pre-run R object and all functions needed to run FGGA.
## From Bioconductor repository
if (!requireNamespace(“BiocManager”, quietly = TRUE)) { install.packages(“BiocManager”)}
BiocManager::install(“fgga”)
## Or from GitHub repository using devtools
BiocManager::install(“devtools”)
devtools::install_github(“fspetale/fgga”)
REFERENCES
1: Spetale F.E., Tapia E., Krsticevic F., Roda F. and Bulacio P. “A Factor Graph Approach to Automated GO Annotation”. PLoS ONE 11(1): e0146986, 2016.
2: Spetale Flavio E., Arce D., Krsticevic F., Bulacio P. and Tapia E. “Consistent prediction of GO protein localization”. Scientific Report 7787(8), 2018