Journal Article

Genome-wide inferring gene–phenotype relationship by walking on the heterogeneous network

Yongjin Li and Jagdish C. Patra

in Bioinformatics

Volume 26, issue 9, pages 1219-1224
Published in print May 2010 | ISSN: 1367-4803
Published online March 2010 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btq108
Genome-wide inferring gene–phenotype relationship by walking on the heterogeneous network

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Motivation: Clinical diseases are characterized by distinct phenotypes. To identify disease genes is to elucidate the gene–phenotype relationships. Mutations in functionally related genes may result in similar phenotypes. It is reasonable to predict disease-causing genes by integrating phenotypic data and genomic data. Some genetic diseases are genetically or phenotypically similar. They may share the common pathogenetic mechanisms. Identifying the relationship between diseases will facilitate better understanding of the pathogenetic mechanism of diseases.

Results: In this article, we constructed a heterogeneous network by connecting the gene network and phenotype network using the phenotype–gene relationship information from the OMIM database. We extended the random walk with restart algorithm to the heterogeneous network. The algorithm prioritizes the genes and phenotypes simultaneously. We use leave-one-out cross-validation to evaluate the ability of finding the gene–phenotype relationship. Results showed improved performance than previous works. We also used the algorithm to disclose hidden disease associations that cannot be found by gene network or phenotype network alone. We identified 18 hidden disease associations, most of which were supported by literature evidence.

Availability: The MATLAB code of the program is available at http://www3.ntu.edu.sg/home/aspatra/research/Yongjin_BI2010.zip

Contact: yongjin.li@gmail.com

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5100 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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