Target Prediction Based On PPI Network
- Authors
- 이태건; 황유현; 오민; 윤영미
- Issue Date
- 2016
- Publisher
- 한국컴퓨터정보학회
- Keywords
- Systems biology; Target prediction; Target repositioning; Drug repositioning
- Citation
- 한국컴퓨터정보학회논문지, v.21, no.3, pp.65 - 71
- Journal Title
- 한국컴퓨터정보학회논문지
- Volume
- 21
- Number
- 3
- Start Page
- 65
- End Page
- 71
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/9545
- ISSN
- 1598-849X
- Abstract
- To reduce the expenses for development a novel drug, systems biology has been studied actively.
Target prediction, a part of systems biology, contributes to finding a new purpose for FDA(Food and Drug Administration) approved drugs and development novel drugs. In this paper, we propose a classification model for predicting novel target genes based on relation between target genes and disease related genes. After collecting known target genes from TTD(Therapeutic Target Database) and disease related genes from OMIM(Online Mendelian Inheritance in Man), we analyzed the effect of target genes on disease related genes based on PPI(Protein-Protein Interactions) network. We focused on the distinguishing characteristics between known target genes and random target genes, and used the characteristics as features for building a classifier. Because our model is constructed using information about only a disease and its known targets, the model can be applied to unusual diseases without similar drugs and diseases, while existing models for finding new drug-disease associations are based on drug-drug similarity and disease-disease similarity. We validated accuracy of the model using LOOCV of ten times and the AUCs were 0.74 on Alzheimer’s disease and 0.71 on Breast cancer.
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Collections - IT융합대학 > 컴퓨터공학과 > 1. Journal Articles
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