TITLE:
Semi-Global Inference in Phenotype-Protein Network
AUTHORS:
Siliang Xia, Guangri Quan, Yongbo Zhao, Xuhui Jia
KEYWORDS:
Diseases Gene Prioritization; Phenotype-Protein Network; Semi-Global Inference; Phenotype Similarity Threshold
JOURNAL NAME:
Engineering,
Vol.5 No.10B,
October
30,
2013
ABSTRACT:
Discovering genetic basis of diseases is an important
goal and a challenging problem in bioinformatics research. Inspired
by network-based global inference approach, Semi-global inference method is
proposed to capture the complex associations between phenotypes and genes. The
proposed method integrates phenotype similarities and protein-protein
interactions, and it establishes the profile vectors of phenotypes and
proteins. Then the relevance between each candidate gene and the
target phenotype is evaluated. Candidate genes are then ranked according to
relevance mark and genes that are potentially associated with target disease
are identified based on this ranking. The model selects nodes in integrated
phenotype-protein network for inference, by exploiting Phenotype Similarity
Threshold (PST), which throws lights on selection of similar phenotypes for
gene prediction problem. Different vector relevance metrics for computing the
relevance marks of candidate genes are discussed. The performance of the model
is evaluated on Online Mendelian Inheritance in Man (OMIM) data sets and
experimental evaluation shows high performance of proposed Semi-global method
outperforms existing global inference methods.