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2010 | 8 | 4 | 672-682
Tytuł artykułu

Predicting disease-related genes by topological similarity in human protein-protein interaction network

Autorzy
Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Predicting genes likely to be involved in human diseases is an important task in bioinformatics field. Nowadays, the accumulation of human protein-protein interactions (PPIs) data provides us an unprecedented opportunity to gain insight into human diseases. In this paper, we adopt the topological similarity in human protein-protein interaction network to predict disease-related genes. As a computational algorithm to speed up the identification of disease-related genes, the topological similarity has substantial advantages over previous topology-based algorithms. First of all, it provides a global measurement of similarity between two vertices. Secondly, quantity which can measure new topological feature has been integrated into the notion of topological similarity. Our method is specially designed for predicting disease-related genes of single disease-gene family. The proposed method is applied to human protein-protein interaction and hepatocellular carcinoma (HCC) data. The results show a significant enrichment of disease-related genes that are characterized by higher topological similarity than other genes.
Wydawca

Czasopismo
Rocznik
Tom
8
Numer
4
Strony
672-682
Opis fizyczny
Daty
wydano
2010-08-01
online
2010-05-22
Twórcy
autor
  • Department of physics, University of Xiangtan, Xiangtan, 411105, Hunan, China, twinsheros@126.com
autor
  • Department of physics, University of Xiangtan, Xiangtan, 411105, Hunan, China
autor
  • Department of physics, University of Xiangtan, Xiangtan, 411105, Hunan, China, tang_yii@126.com
Bibliografia
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Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.-psjd-doi-10_2478_s11534-009-0114-9
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