A recommendation system for the prediction of drug-target associations

Abstract

In this chapter a recommendation system is presented, based on the integration of a Protein-Protein Interaction (PPI) network taken from the Intact database, and a set of associations between drugs and targets taken from the DrugBank database. Depending on how proteins are connected on the PPI network, given an input drug the system suggests new targets. The framework adopted for the implementation is Apache Spark, useful for loading, managing and manipulating data by means of appropriate Resilient Distributed Datasets (RDD), and for the use of the Alternating Least Square (ALS) machine learning algorithm, a Matrix Factorization algorithm for distributed and parallel computing.Finally, an accurate analysis of the results has been carried out by the creation of interactive graphs that intuitively show the interactions between the predicted proteins and the proteins that have a direct relationship with the drug fed in input to the recommendation system.