count_models_that_predict_synergies

a character vector. Elements are (synergistic) drug
combinations, each one being a string in the form A-B - no spaces
between the names and the hyphen '-')

drug.comb.vec

a <code>data.frame</code> object with rows as the models
and columns the drug combinations tested. Possible values for each
model-drug combination element are either 0 (no synergy
predicted), 1 (synergy was predicted) or NA

model.predictions

Use this function to find the number of models that predict a given set of
drug combinations (usually the ones found as synergies).

Analysis and visualization of an ensemble of boolean models for
biomarker discovery in cancer cell networks. The package allows to easily
import the data results of a software pipeline that predicts synergistic drug
combinations in cancer cell lines, developed by the DrugLogics research group
in NTNU. It has generic functions that can be used to split a boolean model
dataset to model groups with regards to the models predictive performance (number of true
positive predictions or Matthews correlation coefficient score) or synergy prediction based on a given set
of observed synergies and find the average activity difference per network
node between all group pairs. Thus, given user-specific thresholds,
important nodes (biomarkers) can be accessed in the sense that they make the
models predict specific synergies (synergy biomarkers) or have better
performance in general (performance biomarkers). Lastly, if the
boolean models have a specific equation form and differ only in their link operator,
link operator biomarkers can also be assessed.

count_models_that_predict_synergies: Count models that predict a set of synergies

Description

Usage

Arguments

Value