Implementation of a Machine Learning Framework for Predicting Drugs Side Effects

An implementation of a Machine Learning Framework for prediction of new drugs Side Effects. Firstly drugs are clustered with respect to their features description and secondly predictions are made, according to Bayesian scores. Moreover it can perform protein enrichment considering the proteins clustered together in the first step of the algorithm. This last tool is of extreme interest for biologist and drug discovery purposes, given the fact that it can be used either as a validation of the clusters obtained, as well as for the possible discovery of new interactions between certain side effects and non targeted pathways. Clustering of the drugs in the feature space can be done using K-Means, PAM or K-Seeds (a novel clustering algorithm proposed by the author).


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("DrugClust")

0.2 by Giovanna Maria Dimitri, 6 years ago


Browse source code at https://github.com/cran/DrugClust


Authors: Giovanna Maria Dimitri


Documentation:   PDF Manual  


GPL-2 license


Imports ROCR, MESS, cclust, cluster, e1071, utils, base


See at CRAN