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K. Lage, E. O. Karlberg, Z. M. Storling, P. I. Olason, A. G. Pedersen, O. Rigina, A. M. Hinsby, Z. Tumer, F. Pociot, N. Tommerup, Y. Moreau and S. Brunak, “A Human Phenome-Interactome Network of Protein Complexes Implicated in Genetic Disorders,” Nature Biotechnology, Vol. 25, 2007, pp. 309-316. http://dx.doi.org/10.1038/nbt1295

has been cited by the following article:

• 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.