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This article is part of the supplement: Fourth International Workshop on Data and Text Mining in Biomedical Informatics (DTMBio) 2010

Open Access Highly Accessed Proceedings

Building the process-drug–side effect network to discover the relationship between biological Processes and side effects

Sejoon Lee1, Kwang H Lee1, Min Song2* and Doheon Lee1*

Author Affiliations

1 Bio and Brain Engineering Department, KAIST, Daejeon 305-701, South Korea

2 Information Systems Department, New Jersey Institute of Technology, University Heights, Newark, USA

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BMC Bioinformatics 2011, 12(Suppl 2):S2  doi:10.1186/1471-2105-12-S2-S2

Published: 29 March 2011

Abstract

Background

Side effects are unwanted responses to drug treatment and are important resources for human phenotype information. The recent development of a database on side effects, the side effect resource (SIDER), is a first step in documenting the relationship between drugs and their side effects. It is, however, insufficient to simply find the association of drugs with biological processes; that relationship is crucial because drugs that influence biological processes can have an impact on phenotype. Therefore, knowing which processes respond to drugs that influence the phenotype will enable more effective and systematic study of the effect of drugs on phenotype. To the best of our knowledge, the relationship between biological processes and side effects of drugs has not yet been systematically researched.

Methods

We propose 3 steps for systematically searching relationships between drugs and biological processes: enrichment scores (ES) calculations, t-score calculation, and threshold-based filtering. Subsequently, the side effect-related biological processes are found by merging the drug-biological process network and the drug-side effect network. Evaluation is conducted in 2 ways: first, by discerning the number of biological processes discovered by our method that co-occur with Gene Ontology (GO) terms in relation to effects extracted from PubMed records using a text-mining technique and second, determining whether there is improvement in performance by limiting response processes by drugs sharing the same side effect to frequent ones alone.

Results

The multi-level network (the process-drug-side effect network) was built by merging the drug-biological process network and the drug-side effect network. We generated a network of 74 drugs-168 side effects-2209 biological process relation resources. The preliminary results showed that the process-drug-side effect network was able to find meaningful relationships between biological processes and side effects in an efficient manner.

Conclusions

We propose a novel process-drug-side effect network for discovering the relationship between biological processes and side effects. By exploring the relationship between drugs and phenotypes through a multi-level network, the mechanisms underlying the effect of specific drugs on the human body may be understood.