Use this function to find either positive or negative biomarkers across many
performance classification group matchings based on a given threshold between
0 and 1. The logic behind the biomarker selection is that if there is at
least one value in a column of
the diff.mat matrix that surpasses the threshold given, then the
corresponding node (name of the column) is return as a biomarker. This means
that for a single node, if at least one value that represents an average data
difference (for example, the average activity state difference) between any
of the given classification group comparisons (below) the threshold (negative
threshold), then a positive (negative) biomarker is reported.
get_biomarkers_per_type(diff.mat, threshold, type)a matrix whose rows are vectors of average node data
differences between two groups of models based on some kind of classification
(e.g. number of TP predictions) and whose names are set in the rownames
attribute of the matrix (usually denoting the different classification
groups, e.g. (1,2) means the models that predicted 1 TP synergy vs the models
that predicted 2 TP synergies, if the classification is done by number of TP
predictions). The columns represent the network's node names.
numeric. A number in the [0,1] interval, above which (or below its negative value) a biomarker will be registered in the returned result. Values closer to 1 translate to a more strict threshold and thus less biomarkers are found.
character. Accepted values are positive or negative.
a character vector that includes the node names that were found either as positive or negative.
Other biomarker functions: get_biomarkers