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Large-Scale Structural and Textual Similarity-Based Mining of Knowledge Graph to Predict Drug-Drug Interactions

17 Pages Posted: 9 Jul 2018 First Look: Accepted

See all articles by Ibrahim Abdelaziz

Ibrahim Abdelaziz

King Abdullah University of Science and Technology

Achille Fokoue

IBM Corporation - Thomas J. Watson Research Center

Oktie Hassanzadeh

IBM Corporation - Thomas J. Watson Research Center

Ping Zhang

IBM Corporation - Thomas J. Watson Research Center

Mohammad Sadoghi

Purdue University - Department of Computer Sciences

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. In this paper, we present Tiresias, a large-scale similarity-based framework that predicts DDIs through link prediction. Tiresias 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. In particular, Tiresias utilizes two classes of features in a knowledge graph: local and global features. Local features are derived from the information directly associated to each drug (i.e., one hop away) while global features are learnt by minimizing a global loss function that considers the complete structure of the knowledge graph. 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 Tiresias 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 as well as newly developed drugs.

Keywords: Drug Interaction, Similarity-Based, Link Prediction

Suggested Citation

Abdelaziz, Ibrahim and Fokoue, Achille and Hassanzadeh, Oktie and Zhang, Ping and Sadoghi, Mohammad, Large-Scale Structural and Textual Similarity-Based Mining of Knowledge Graph to Predict Drug-Drug Interactions (2017). Journal of Web Semantics First Look. Available at SSRN: https://ssrn.com/abstract=3199303 or http://dx.doi.org/10.2139/ssrn.3199303

Ibrahim Abdelaziz (Contact Author)

King Abdullah University of Science and Technology ( email )

Thuwal 23955- 6900
Saudi Arabia

Achille Fokoue

IBM Corporation - Thomas J. Watson Research Center ( email )

Route 134
Kitchawan Road
Yorktown Heights, NY 10598
United States

Oktie Hassanzadeh

IBM Corporation - Thomas J. Watson Research Center ( email )

Route 134
Kitchawan Road
Yorktown Heights, NY 10598
United States

Ping Zhang

IBM Corporation - Thomas J. Watson Research Center ( email )

Route 134
Kitchawan Road
Yorktown Heights, NY 10598
United States

Mohammad Sadoghi

Purdue University - Department of Computer Sciences ( email )

United States

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