Open AccessDegree‐adjusted algorithm for prioritisation of candidate disease genes from gene expression and protein interactome
Author(s) -
Wang Yichuan,
Fang Haiyang,
Yang Tinghong,
Wu Duzhi,
Zhao Jing
Publication year - 2014
Publication title -
iet systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.367
H-Index - 50
eISSN - 1751-8857
pISSN - 1751-8849
DOI - 10.1049/iet-syb.2013.0038
Subject(s) - interactome , gene , degree (music) , computational biology , gene expression , biology , genetics , bioinformatics , physics , acoustics
Computational methods play an important role in the disease genes prioritisation by integrating many kinds of data sources such as gene expression, functional annotations and protein–protein interactions. However, the existing methods usually perform well in predicting highly linked genes, whereas they work quite poorly for loosely linked genes. Motivated by this observation, a degree‐adjusted strategy is applied to improve the algorithm that was proposed earlier for the prediction of disease genes from gene expression and protein interactions. The authors also showed that the modified method is good at identifying loosely linked disease genes and the overall performance gets enhanced accordingly. This study suggests the importance of statistically adjusting the degree distribution bias in the background network for network‐based modelling of complex diseases.