net.infer {PPInfer} | R Documentation |
Proteins can be classified by using networks to identify functionally closely related proteins.
net.infer(target, kernel, top = NULL, cross = 0, C = 1, nu = 0.2, epsilon = 0.1, cache1 = 40, tol1 = 0.001, shrinking1 = TRUE, cache2 = 40, tol2 = 0.001, shrinking2 = TRUE)
target |
set of interesting proteins or target class |
kernel |
the regularized Laplacian matrix for a graph |
top |
number of top proteins most closely related to target class (default: all proteins except for target and pseudo-absence class) |
cross |
if a integer value k>0 is specified, a k-fold cross validation on the training data is performed to assess the quality of the model |
C |
cost of constraints violation for SVM (default: 1) |
nu |
The nu parameter for OCSVM (default: 0.2) |
epsilon |
epsilon in the insensitive-loss function for OCSVM (default: 0.1) |
cache1 |
cache memory in MB for OCSVM (default: 40) |
tol1 |
tolerance of termination criterion for OCSVM (default: 0.001) |
shrinking1 |
option whether to use the shrinking-heuristics for OCSVM (default: TRUE) |
cache2 |
cache memory in MB for SVM (default: 40) |
tol2 |
tolerance of termination criterion for SVM (default: 0.001) |
shrinking2 |
option whether to use the shrinking-heuristics for SVM (default: TRUE) |
list |
list of a target class used in the model |
error |
training error |
CVerror |
cross validation error, (when cross > 0) |
top |
top proteins |
score |
decision values for top proteins |
Dongmin Jung, Xijin Ge
Senay, S. D. et al. (2013). Novel three-step pseudo-absence selection technique for improved species distribution modelling. PLOS ONE. 8(8), e71218.
ksvm
# example 1 ## Not run: string.db.9606 <- STRINGdb$new(version = '10',species = 9606, score_threshold = 999) string.db.9606.graph <- string.db.9606$get_graph() K.9606 <- net.kernel(string.db.9606.graph) rownames(K.9606) <- substring(rownames(K.9606), 6) colnames(K.9606) <- substring(colnames(K.9606), 6) target <- colnames(K.9606)[1:100] infer <- net.infer(target, K.9606, 10) ## End(Not run) # example 2 data(litG) litG <- igraph.from.graphNEL(litG) sg <- decompose(litG, min.vertices = 50) sg <- sg[[1]] K <- net.kernel(sg) litG.infer <- net.infer(names(V(sg))[1:10], K, top=20)