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J Biomed Inform. 2017 Apr;68:167-183. doi: 10.1016/j.jbi.2017.03.006. Epub 2017 Mar 11.

Heter-LP: A heterogeneous label propagation algorithm and its application in drug repositioning.

Author information

1
Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran. Electronic address: maryam.lotfi@ec.iut.ac.ir.
2
Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran. Electronic address: nghadiri@cc.iut.ac.ir.
3
Department of Computer Engineering, Amirkabir University of Technology, Tehran 15916-34311, Iran; School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran. Electronic address: srm@aut.ac.ir.
4
Department of Pharmaceutics, School of Pharmacy and Pharmaceutical Science, Isfahan University of Medical Sciences, Isfahan, Iran. Electronic address: varshosaz@pharm.mui.ac.ir.
5
Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada. Electronic address: jrgreen@sce.carleton.ca.

Abstract

Drug repositioning offers an effective solution to drug discovery, saving both time and resources by finding new indications for existing drugs. Typically, a drug takes effect via its protein targets in the cell. As a result, it is necessary for drug development studies to conduct an investigation into the interrelationships of drugs, protein targets, and diseases. Although previous studies have made a strong case for the effectiveness of integrative network-based methods for predicting these interrelationships, little progress has been achieved in this regard within drug repositioning research. Moreover, the interactions of new drugs and targets (lacking any known targets and drugs, respectively) cannot be accurately predicted by most established methods. In this paper, we propose a novel semi-supervised heterogeneous label propagation algorithm named Heter-LP, which applies both local and global network features for data integration. To predict drug-target, disease-target, and drug-disease associations, we use information about drugs, diseases, and targets as collected from multiple sources at different levels. Our algorithm integrates these various types of data into a heterogeneous network and implements a label propagation algorithm to find new interactions. Statistical analyses of 10-fold cross-validation results and experimental analyses support the effectiveness of the proposed algorithm.

KEYWORDS:

Disease-target interactions; Drug-disease associations; Drug–target interactions; Heterogeneous networks; Label propagation; Semi-supervised learning

PMID:
28300647
DOI:
10.1016/j.jbi.2017.03.006
[Indexed for MEDLINE]
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