Predicting drug–disease associations by network embedding and biomedical data integration

Publication Date01 April 2019
Date01 April 2019
AuthorXiaomei Wei,Yaliang Zhang,Yu Huang,Yaping Fang
SubjectLibrary & information science,Librarianship/library management,Library technology,Information behaviour & retrieval,Information & knowledge management,Information & communications technology,Internet
Predicting drugdisease
associations by network embedding
and biomedical data integration
Xiaomei Wei, Yaliang Zhang, Yu Huang and Yaping Fang
College of Informatics, Huazhong Agricultural University, Wuhan, China
Purpose The traditional drug development process is costly, time consuming and risky. Using
computational methods to discover drug repositioning opportunities is a promising and efficient strategy in
the era of big data. The explosive growth of large-scale genomic, phenotypic data and all kinds of omics
data brings opportunities for developing new computational drug repositioning methods based on big data.
The paper aims to discuss this issue.
Design/methodology/approach Here, a new computational strategy is proposed for inferring
drugdisease associations from rich biomedical resources toward drug repositioning. First, the network
embedding (NE) algorithm is adopted to learn the latent feature representation of drugs from multiple
biomedical resources. Furthermore, on the basis of the latent vectors of drugs from the NE module, a binary
support vector machine classifier is trained to divide unknown drugdisease pairs into positive and negative
instances. Finally, this model is validated on a well-established drugdisease association data set with tenfold
Findings This model obtains the performance of an area under the receiver operating characteristic curve
of 90.3 percent, which is comparable to those of similarsystems. The authors also analyze the performance of
the model and validate its effect on predicting the new indications of old drugs.
Originality/value This studyshows that the authorsmethodis predictive, identifying noveldrugdisease
interactions for drug discovery. The new feature learning methods also positively contribute to the
heterogeneousdata integration.
Keywords Computational method, Data integration, Features, Drug repositioning,
Drugdisease associations, Latent representation, Network embedding
Paper type Research paper
1. Introduction
Traditional drug development is highly resource-intensive, expensive, and prone to failure.
The de novo drug discovery process requires the investment of billions of dollars and an
average of about 912 years to bring an experimental drug to the market, and failures are
common across all drug development pipelines (Dickson and Gagnon, 2004). Over the past
decade, with the explosive growth of large-scale genomic and phenotypic data, as well as the
improvement of systematic approaches, computational solutions have shown reasonable
and feasible in discovering new indications of old drugs in the age of big data.
Using computational approaches combined with biomedical data resources to infer novel
indications for existing drug offers great advantages in speeding up drug development with
decreased risk (Nagaraj et al., 2018; Khatoon and Govardhan, 2018). Drug repositioning can
reduce the lag of drug discovery and development time from 10 17 years to potentially
312 years (Hurle et al., 2013). In addition, the repurposed drugs accounted for 20 percent of the
84 drug products introduced to the market in 2013 (Graul et al., 2014). Many failed drugs and
existing drugs have been investigated and successfully approved for new indications. Drug
repositioning is playing an increasingly important role in the drug development and precision
medicine paradigm (Shameer et al., 2015) on account of the widespread attention from the
pharmaceutical companies, government agencies and academic institutes. Many computational
approaches have been introduced to integrate heterogeneous data sources to predict new
Data Technologies and
Vol. 53 No. 2, 2019
pp. 217-229
© Emerald PublishingLimited
DOI 10.1108/DTA-01-2019-0004
Received 11 January 2019
Revised 16 March 2019
Accepted 16 March 2019
The current issue and full text archive of this journal is available on Emerald Insight at:
This work was supported in part by the National Natural Science Foundation of China (No. 31501076).

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