Drug repurposing with network reinforcement

Yonghyun Nam, Myungjun Kim, Hang-Seok Chang, Hyunjung Shin

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Background: Drug repurposing has been motivated to ameliorate low probability of success in drug discovery. For the recent decade, many in silico attempts have received primary attention as a first step to alleviate the high cost and longevity. Such study has taken benefits of abundance, variety, and easy accessibility of pharmaceutical and biomedical data. Utilizing the research friendly environment, in this study, we propose a network-based machine learning algorithm for drug repurposing. Particularly, we show a framework on how to construct a drug network, and how to strengthen the network by employing multiple/heterogeneous types of data. Results: The proposed method consists of three steps. First, we construct a drug network from drug-target protein information. Then, the drug network is reinforced by utilizing drug-drug interaction knowledge on bioactivity and/or medication from literature databases. Through the enhancement, the number of connected nodes and the number of edges between them become more abundant and informative, which can lead to a higher probability of success of in silico drug repurposing. The enhanced network recommends candidate drugs for repurposing through drug scoring. The scoring process utilizes graph-based semi-supervised learning to determine the priority of recommendations. Conclusions: The drug network is reinforced in terms of the coverage and connections of drugs: the drug coverage increases from 4738 to 5442, and the drug-drug associations as well from 808,752 to 982,361. Along with the network enhancement, drug recommendation becomes more reliable: AUC of 0.89 was achieved lifted from 0.79. For typical cases, 11 recommended drugs were shown for vascular dementia: amantadine, conotoxin GV, tenocyclidine, cycloeucine, etc.

Original languageEnglish
Article number383
JournalBMC bioinformatics
Volume20
DOIs
Publication statusPublished - 2019 Jul 24

Fingerprint

Drug Repositioning
Reinforcement
Drugs
Drug interactions
Supervised learning
Bioactivity
Pharmaceutical Preparations
Drug products
Learning algorithms
Learning systems
Proteins
Costs
tenocyclidine
Computer Simulation
Scoring
Recommendations
Amantadine
Coverage
Vascular Dementia
Enhancement

All Science Journal Classification (ASJC) codes

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

Cite this

Nam, Yonghyun ; Kim, Myungjun ; Chang, Hang-Seok ; Shin, Hyunjung. / Drug repurposing with network reinforcement. In: BMC bioinformatics. 2019 ; Vol. 20.
@article{cb46db5d936a4cc1bd37396c9bee2fb0,
title = "Drug repurposing with network reinforcement",
abstract = "Background: Drug repurposing has been motivated to ameliorate low probability of success in drug discovery. For the recent decade, many in silico attempts have received primary attention as a first step to alleviate the high cost and longevity. Such study has taken benefits of abundance, variety, and easy accessibility of pharmaceutical and biomedical data. Utilizing the research friendly environment, in this study, we propose a network-based machine learning algorithm for drug repurposing. Particularly, we show a framework on how to construct a drug network, and how to strengthen the network by employing multiple/heterogeneous types of data. Results: The proposed method consists of three steps. First, we construct a drug network from drug-target protein information. Then, the drug network is reinforced by utilizing drug-drug interaction knowledge on bioactivity and/or medication from literature databases. Through the enhancement, the number of connected nodes and the number of edges between them become more abundant and informative, which can lead to a higher probability of success of in silico drug repurposing. The enhanced network recommends candidate drugs for repurposing through drug scoring. The scoring process utilizes graph-based semi-supervised learning to determine the priority of recommendations. Conclusions: The drug network is reinforced in terms of the coverage and connections of drugs: the drug coverage increases from 4738 to 5442, and the drug-drug associations as well from 808,752 to 982,361. Along with the network enhancement, drug recommendation becomes more reliable: AUC of 0.89 was achieved lifted from 0.79. For typical cases, 11 recommended drugs were shown for vascular dementia: amantadine, conotoxin GV, tenocyclidine, cycloeucine, etc.",
author = "Yonghyun Nam and Myungjun Kim and Hang-Seok Chang and Hyunjung Shin",
year = "2019",
month = "7",
day = "24",
doi = "10.1186/s12859-019-2858-6",
language = "English",
volume = "20",
journal = "BMC Bioinformatics",
issn = "1471-2105",
publisher = "BioMed Central",

}

Drug repurposing with network reinforcement. / Nam, Yonghyun; Kim, Myungjun; Chang, Hang-Seok; Shin, Hyunjung.

In: BMC bioinformatics, Vol. 20, 383, 24.07.2019.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Drug repurposing with network reinforcement

AU - Nam, Yonghyun

AU - Kim, Myungjun

AU - Chang, Hang-Seok

AU - Shin, Hyunjung

PY - 2019/7/24

Y1 - 2019/7/24

N2 - Background: Drug repurposing has been motivated to ameliorate low probability of success in drug discovery. For the recent decade, many in silico attempts have received primary attention as a first step to alleviate the high cost and longevity. Such study has taken benefits of abundance, variety, and easy accessibility of pharmaceutical and biomedical data. Utilizing the research friendly environment, in this study, we propose a network-based machine learning algorithm for drug repurposing. Particularly, we show a framework on how to construct a drug network, and how to strengthen the network by employing multiple/heterogeneous types of data. Results: The proposed method consists of three steps. First, we construct a drug network from drug-target protein information. Then, the drug network is reinforced by utilizing drug-drug interaction knowledge on bioactivity and/or medication from literature databases. Through the enhancement, the number of connected nodes and the number of edges between them become more abundant and informative, which can lead to a higher probability of success of in silico drug repurposing. The enhanced network recommends candidate drugs for repurposing through drug scoring. The scoring process utilizes graph-based semi-supervised learning to determine the priority of recommendations. Conclusions: The drug network is reinforced in terms of the coverage and connections of drugs: the drug coverage increases from 4738 to 5442, and the drug-drug associations as well from 808,752 to 982,361. Along with the network enhancement, drug recommendation becomes more reliable: AUC of 0.89 was achieved lifted from 0.79. For typical cases, 11 recommended drugs were shown for vascular dementia: amantadine, conotoxin GV, tenocyclidine, cycloeucine, etc.

AB - Background: Drug repurposing has been motivated to ameliorate low probability of success in drug discovery. For the recent decade, many in silico attempts have received primary attention as a first step to alleviate the high cost and longevity. Such study has taken benefits of abundance, variety, and easy accessibility of pharmaceutical and biomedical data. Utilizing the research friendly environment, in this study, we propose a network-based machine learning algorithm for drug repurposing. Particularly, we show a framework on how to construct a drug network, and how to strengthen the network by employing multiple/heterogeneous types of data. Results: The proposed method consists of three steps. First, we construct a drug network from drug-target protein information. Then, the drug network is reinforced by utilizing drug-drug interaction knowledge on bioactivity and/or medication from literature databases. Through the enhancement, the number of connected nodes and the number of edges between them become more abundant and informative, which can lead to a higher probability of success of in silico drug repurposing. The enhanced network recommends candidate drugs for repurposing through drug scoring. The scoring process utilizes graph-based semi-supervised learning to determine the priority of recommendations. Conclusions: The drug network is reinforced in terms of the coverage and connections of drugs: the drug coverage increases from 4738 to 5442, and the drug-drug associations as well from 808,752 to 982,361. Along with the network enhancement, drug recommendation becomes more reliable: AUC of 0.89 was achieved lifted from 0.79. For typical cases, 11 recommended drugs were shown for vascular dementia: amantadine, conotoxin GV, tenocyclidine, cycloeucine, etc.

UR - http://www.scopus.com/inward/record.url?scp=85069759260&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85069759260&partnerID=8YFLogxK

U2 - 10.1186/s12859-019-2858-6

DO - 10.1186/s12859-019-2858-6

M3 - Article

C2 - 31337333

AN - SCOPUS:85069759260

VL - 20

JO - BMC Bioinformatics

JF - BMC Bioinformatics

SN - 1471-2105

M1 - 383

ER -