Next Article in Journal
Dynamic Variations in Multiple Bioactive Constituents under Salt Stress Provide Insight into Quality Formation of Licorice
Previous Article in Journal
Formulation of a Mixture of Plant Extracts for Attenuating Postprandial Glycemia and Diet-Induced Disorders in Rats
Previous Article in Special Issue
Prediction of Disease-related microRNAs through Integrating Attributes of microRNA Nodes and Multiple Kinds of Connecting Edges
Open AccessArticle

Drug Side-Effect Prediction Via Random Walk on the Signed Heterogeneous Drug Network

1
School of Data and Computer Science, Shandong Women’s University, Jinan 250014, China
2
School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China
*
Author to whom correspondence should be addressed.
Molecules 2019, 24(20), 3668; https://doi.org/10.3390/molecules24203668 (registering DOI)
Received: 13 September 2019 / Revised: 8 October 2019 / Accepted: 10 October 2019 / Published: 11 October 2019
(This article belongs to the Special Issue Molecular Computing and Bioinformatics II)
Drug side-effects have become a major public health concern as they are the underlying cause of over a million serious injuries and deaths each year. Therefore, it is of critical importance to detect side-effects as early as possible. Existing computational methods mainly utilize the drug chemical profile and the drug biological profile to predict the side-effects of a drug. In the utilized drug biological profile information, they only focus on drug–target interactions and neglect the modes of action of drugs on target proteins. In this paper, we develop a new method for predicting potential side-effects of drugs based on more comprehensive drug information in which the modes of action of drugs on target proteins are integrated. Drug information of multiple types is modeled as a signed heterogeneous information network. We propose a signed heterogeneous information network embedding framework for learning drug embeddings and predicting side-effects of drugs. We use two bias random walk procedures to obtain drug sequences and train a Skip-gram model to learn drug embeddings. We experimentally demonstrate the performance of the proposed method by comparison with state-of-the-art methods. Furthermore, the results of a case study support our hypothesis that modes of action of drugs on target proteins are meaningful in side-effect prediction. View Full-Text
Keywords: side-effect prediction; signed heterogeneous information network; random walk; modes of action of drugs side-effect prediction; signed heterogeneous information network; random walk; modes of action of drugs
Show Figures

Figure 1

MDPI and ACS Style

Hu, B.; Wang, H.; Yu, Z. Drug Side-Effect Prediction Via Random Walk on the Signed Heterogeneous Drug Network. Molecules 2019, 24, 3668.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop