Predicting drug–disease associations by network embedding and biomedical data integration
| Pages | 217-229 |
| Published date | 01 April 2019 |
| Date | 01 April 2019 |
| DOI | https://doi.org/10.1108/DTA-01-2019-0004 |
| Author | Xiaomei Wei,Yaliang Zhang,Yu Huang,Yaping Fang |
| Subject Matter | Library & information science,Librarianship/library management,Library technology,Information behaviour & retrieval,Information & knowledge management,Information & communications technology,Internet |
Predicting drug–disease
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
Abstract
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
drug–disease 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 drug–disease pairs into positive and negative
instances. Finally, this model is validated on a well-established drug–disease association data set with tenfold
cross-validation.
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 authors’methodis predictive, identifying noveldrug–disease
interactions for drug discovery. The new feature learning methods also positively contribute to the
heterogeneousdata integration.
Keywords Computational method, Data integration, Features, Drug repositioning,
Drug–disease 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 9–12 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
3–12 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
Applications
Vol. 53 No. 2, 2019
pp. 217-229
© Emerald PublishingLimited
2514-9288
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:
www.emeraldinsight.com/2514-9288.htm
This work was supported in part by the National Natural Science Foundation of China (No. 31501076).
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Predicting
drug–disease
associations
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