Computing similarities between proteins based on their GO annotation, KEGG annotation and PPI network topology. It integrates seven features (TCSS, IntelliGO, Wang, KEGG, Jaccard, RA and AA) to predict PPIs using an SVM classifier. Some internal functions to calculate GO semantic similarities are re-used from R package GOSemSim authored by Guangchuang Yu.
|Author||Yue Deng, Rongjie Shao, Gang Wang and Yuanjun Sun|
|Date of publication||2015-07-28 08:03:42|
|Maintainer||Yue Deng <email@example.com>|
AASim: Compute Adamic-Adar Index Between Two Nodes in PPI Network
ComputeAllEvidences: Compute the Biological and Topological Similarities Between...
FNPre: Predict false negative interactions based on topological...
GOKEGGSims: GO- and KEGG- based Similarities Between two Genes
GOKEGGSimsFromFile: GO- and KEGG- based Similarities Between two Genes
IntelliGOGeneSim: IntelliGO Semantic Similarity Between two Genes
JaccardSim: Compute Jaccard Index Between Two Nodes in PPI Network
KEGGSim: KEGG Semantic Similarity Between two Genes
ppiPre-internal: Internal ppiPre objects
ppiPre-package: Predicting protein-protein interactions
RASim: Compute Resource Allocation Index Between Two Nodes in PPI...
SVMPredict: Predict false interactions using a training set
SVMTrain: Using Golden Standard Data Sets to Train an SVM Classifier
TCSSGeneSim: Topological Clustering Semantic Similarity(TCSS) Between two...
TopologicSims: Compute topological similarities from user input file