Logistic regression algorithm to identify candidate disease genes based on reliable protein-protein interaction network

2021 ◽  
Vol 64 (7) ◽  
Author(s):  
Xiujuan Lei ◽  
Wenxiang Zhang
2016 ◽  
Vol 24 (01) ◽  
pp. 117-127 ◽  
Author(s):  
S. UMADEVI ◽  
K. PREMKUMAR ◽  
S. VALARMATHI ◽  
P. M. AYYASAMY ◽  
S. RAJAKUMAR

Diabetic retinopathy is the most common cause of blindness, associated with many biochemical pathways mediated by several genes and proteins. Disease gene identification can be achieved through several approaches but still it is a challenging task. This study, aimed to find out the novel genes associated with diabetic retinopathy. In this study, all the well-known genes associated with diabetic retinopathy were collected from databases and the protein interaction partners were identified. The interacting candidate genes were chosen by chromosomal locations, sharing with disease genes. The protein–protein interaction network was constructed and the key nodes (genes) were identified by degree, betweenness centrality, closeness centrality and eccentricity centrality. Further, the ontological terms, molecular function, biological process and cellular components were related with that of the disease genes with p-value [Formula: see text]. The genes UBC, FOS, ITGB1, FOXA2, CCND1, FOSL1, RXRA and NCAM1 were identified as potential genes associated with diabetic retinopathy. The molecular functions of these genes include protein binding, receptor activity, receptor binding, oxidoreductase activity, protein kinase activity, serine-type peptidase activity and growth factor. Many of the identified genes were clinically related as evidence by the literature.


2017 ◽  
Vol 8 (Suppl 1) ◽  
pp. S20-S21 ◽  
Author(s):  
Akram Safaei ◽  
Mostafa Rezaei Tavirani ◽  
Mona Zamanian Azodi ◽  
Alireza Lashay ◽  
Seyed Farzad Mohammadi ◽  
...  

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