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PCVMZM: Using the Probabilistic Classification Vector Machines Model Combined with a Zernike Moments Descriptor to Predict Protein–Protein Interactions from Protein Sequences
Open AccessArticle

Determination of Genes Related to Uveitis by Utilization of the Random Walk with Restart Algorithm on a Protein–Protein Interaction Network

Department of Ophthalmology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
School of Life Sciences, Shanghai University, Shanghai 200444, China
Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
Author to whom correspondence should be addressed.
Academic Editor: Christo Z. Christov
Int. J. Mol. Sci. 2017, 18(5), 1045;
Received: 9 April 2017 / Revised: 8 May 2017 / Accepted: 9 May 2017 / Published: 13 May 2017
(This article belongs to the Special Issue Special Protein Molecules Computational Identification)
Uveitis, defined as inflammation of the uveal tract, may cause blindness in both young and middle-aged people. Approximately 10–15% of blindness in the West is caused by uveitis. Therefore, a comprehensive investigation to determine the disease pathogenesis is urgent, as it will thus be possible to design effective treatments. Identification of the disease genes that cause uveitis is an important requirement to achieve this goal. To begin to answer this question, in this study, a computational method was proposed to identify novel uveitis-related genes. This method was executed on a large protein–protein interaction network and employed a popular ranking algorithm, the Random Walk with Restart (RWR) algorithm. To improve the utility of the method, a permutation test and a procedure for selecting core genes were added, which helped to exclude false discoveries and select the most important candidate genes. The five-fold cross-validation was adopted to evaluate the method, yielding the average F1-measure of 0.189. In addition, we compared our method with a classic GBA-based method to further indicate its utility. Based on our method, 56 putative genes were chosen for further assessment. We have determined that several of these genes (e.g., CCL4, Jun, and MMP9) are likely to be important for the pathogenesis of uveitis. View Full-Text
Keywords: uveitis; protein–protein interaction; random walk with restart algorithm uveitis; protein–protein interaction; random walk with restart algorithm
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MDPI and ACS Style

Lu, S.; Yan, Y.; Li, Z.; Chen, L.; Yang, J.; Zhang, Y.; Wang, S.; Liu, L. Determination of Genes Related to Uveitis by Utilization of the Random Walk with Restart Algorithm on a Protein–Protein Interaction Network. Int. J. Mol. Sci. 2017, 18, 1045.

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