Disease Pathway Cut for Multi-Target drugs View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Sunjoo Bang, Sangjoon Son, Sooyoung Kim, Hyunjung Shin

ABSTRACT

BACKGROUND: Biomarker discovery studies have been moving the focus from a single target gene to a set of target genes. However, the number of target genes in a drug should be minimum to avoid drug side-effect or toxicity. But still, the set of target genes should effectively block all possible paths of disease progression. METHODS: In this article, we propose a network based computational analysis for target gene identification for multi-target drugs. The min-cut algorithm is employed to cut all the paths from onset genes to apoptotic genes on a disease pathway. If the pathway network is completely disconnected, development of disease will not further go on. The genes corresponding to the end points of the cutting edges are identified as candidate target genes for a multi-target drug. RESULTS AND CONCLUSIONS: The proposed method was applied to 10 disease pathways. In total, thirty candidate genes were suggested. The result was validated with gene set enrichment analysis software, PubMed literature review and de facto drug targets. More... »

PAGES

74

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12859-019-2638-3

DOI

http://dx.doi.org/10.1186/s12859-019-2638-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112097160

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30760209


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52 schema:description BACKGROUND: Biomarker discovery studies have been moving the focus from a single target gene to a set of target genes. However, the number of target genes in a drug should be minimum to avoid drug side-effect or toxicity. But still, the set of target genes should effectively block all possible paths of disease progression. METHODS: In this article, we propose a network based computational analysis for target gene identification for multi-target drugs. The min-cut algorithm is employed to cut all the paths from onset genes to apoptotic genes on a disease pathway. If the pathway network is completely disconnected, development of disease will not further go on. The genes corresponding to the end points of the cutting edges are identified as candidate target genes for a multi-target drug. RESULTS AND CONCLUSIONS: The proposed method was applied to 10 disease pathways. In total, thirty candidate genes were suggested. The result was validated with gene set enrichment analysis software, PubMed literature review and de facto drug targets.
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