June 2017
Volume 58, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2017
Mining for novel genes related to uveitis in a large network with a shortest path algorithm
Author Affiliations & Notes
  • Jian Zhang
    Shanghai First People's Hospital, Shanghai, China
  • Footnotes
    Commercial Relationships   Jian Zhang, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2017, Vol.58, 3563. doi:
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    • Get Citation

      Jian Zhang; Mining for novel genes related to uveitis in a large network with a shortest path algorithm. Invest. Ophthalmol. Vis. Sci. 2017;58(8):3563.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Uveitis is an intraocular inflammation disease, which can cause blindness for both young and middle-aged individuals. In west, 10-15% of the blindness are caused by uveitis. Up to now, its pathological processes are still uncovered, inducing difficulties for designing effective treatments. Identification of genes related to uveitis as complete as possible is an important way to understand its pathological processes.

Methods : In this study, some possible novel uveitis related genes were discovered using a computational method. To execute the method, we first retrieved known uveitis related genes from the UniprotKB database and references in PubMed, and constructed a large network using protein-protein interaction information reported in STRING. Then, the shortest path algorithm was applied to search possible novel genes based on known genes related to uveitis. To make the obtained genes more reliable, a permutation test was adopted to exclude false positives and the linkages between possible genes and known ones were used to select essential possible genes.

Results : Twenty-one possible genes were identified by the computational method. They are deemed to be related to uveitis.

Conclusions : According to the analyses of obtained genes, eleven genes have evidences to be related to uveitis. The rest obtained genes required further validation.

This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.

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