Predicting Drug-Drug Interactions Through Large-Scale Similarity-Based Link Prediction View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2016

AUTHORS

Achille Fokoue , Mohammad Sadoghi , Oktie Hassanzadeh , Ping Zhang

ABSTRACT

Drug-Drug Interactions (DDIs) are a major cause of preventable adverse drug reactions (ADRs), causing a significant burden on the patients’ health and the healthcare system. It is widely known that clinical studies cannot sufficiently and accurately identify DDIs for new drugs before they are made available on the market. In addition, existing public and proprietary sources of DDI information are known to be incomplete and/or inaccurate and so not reliable. As a result, there is an emerging body of research on in-silico prediction of drug-drug interactions. We present Tiresias, a framework that takes in various sources of drug-related data and knowledge as inputs, and provides DDI predictions as outputs. The process starts with semantic integration of the input data that results in a knowledge graph describing drug attributes and relationships with various related entities such as enzymes, chemical structures, and pathways. The knowledge graph is then used to compute several similarity measures between all the drugs in a scalable and distributed framework. The resulting similarity metrics are used to build features for a large-scale logistic regression model to predict potential DDIs. We highlight the novelty of our proposed approach and perform thorough evaluation of the quality of the predictions. The results show the effectiveness of Tiresias in both predicting new interactions among existing drugs and among newly developed and existing drugs. More... »

PAGES

774-789

Book

TITLE

The Semantic Web. Latest Advances and New Domains

ISBN

978-3-319-34128-6
978-3-319-34129-3

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-34129-3_47

DOI

http://dx.doi.org/10.1007/978-3-319-34129-3_47

DIMENSIONS

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