Drug-Target Interaction Prediction Using Semantic Similarity and Edge Partition

author: Maria Esther Vidal, University Simón Bolí­var
published: Dec. 19, 2014,   recorded: October 2014,   views: 34
Categories

Slides

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Delicious Bibliography

Description

The ability to integrate a wealth of human-curated knowledge from scientific datasets and ontologies can benefit drug-target interaction prediction. The hypothesis is that similar drugs interact with the same targets, and similar targets interact with the same drugs. The similarities between drugs reflect a chemical semantic space, while similarities between targets reflect a genomic semantic space. In this paper, we present a novel method that combines a data mining framework for link prediction, semantic knowledge (similarities) from ontologies or semantic spaces, and an algorithmic approach to partition the edges of a heterogeneous graph that includes drug-target interaction edges, and drug-drug and target-target similarity edges. Our semantics based edge partitioning approach, semEP, has the advantages of edge based community detection which allows a node to participate in more than one cluster or community. The semEP problem is to create a minimal partitioning of the edges such that the cluster density of each subset of edges is maximal. We use semantic knowledge (similarities) to specify edge constraints, i.e., specific drug-target interaction edges that should not participate in the same cluster. Using a well-known dataset of drug-target interactions, we demonstrate the benefits of using semEP predictions to improve the performance of a range of state-of-the-art machine learning based prediction methods. Validation of the novel best predicted interactions of semEP against the STITCH interaction resource reflect both accurate and diverse predictions.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: