KSeedsScores: KSeedsScores

In DrugClust: Implementation of a Machine Learning Framework for Predicting Drugs Side Effects

Description

Function for obtaining the Bayesian prediction scores using KSeeds clustering

Usage

KSeedsScores(train, trainpharmat, num_clusters, Seed, s, clusters)

Arguments

train	train matrix of features
trainpharmat	train matrix of side effects
num_clusters	number of clusters desired
Seed	subset of the features matrix containing only the Seeds drugs
s	the seeds of the clusters
clusters	the list of clusters where the various drugs are

Value

A matrix containing prediction scores for each cluster

Examples

r <- 8
c <- 10
m0 <- matrix(0, r, c)
num_clusters=4
features<-apply(m0, c(1,2), function(x) sample(c(0,1),1))
#Generate a sample side effects binary matrix
r1 <- 8
c1 <- 10
m1 <- matrix(0, r1, c1)
side_effects<-apply(m1, c(1,2), function(x) sample(c(0,1),1))
s<-RandomSeedGenerator(num_clusters,nrow(features))
Seed<-SeedSelection(features,num_clusters,s)
clusters<-KSeedsClusters (features,num_clusters,Seed,s)
A<-KSeedsScores(features,side_effects,num_clusters,Seed,s,clusters)

Example output

DrugClust documentation built on May 2, 2019, 11:06 a.m.