prioritization of candidate genes for periodontitis using multiple computational tools background both genetic and environmental factors contribute to the development of periodontitis genetic studies identified a variety of candidate genes for periodontitis the aim of the present study is to identify the most promising candidate genes for periodontitis using an integrative gene ranking methodmethods seed genes that were confirmed to be associated with periodontitis were identified using text mining three types of candidate genes were then extracted from different resources expression profiles genomewide association studies combining the seed genes four freely available bioinformatics tools toppgene dir endeavour and gpec were integrated for prioritization of candidate genes candidate genes that identified with at least three programs and ranked in the top 20 by each program were considered the most promisingresults prioritization analysis resulted in 21 promising genes involved or potentially involved in periodontitis among them il18 interleukin 18 cd44 cd44 molecule cxcl1 chemokine cxc motif ligand 1 il6st interleukin 6 signal transducer mmp3 matrix metallopeptidase 3 mmp7 ccr1 chemokine cc motif receptor 1 mmp13 and tlr9 tolllike receptor 9 had been associated with periodontitis however the roles of other genes such as csf3 colony stimulating factor 3 receptor cd40 tnfsf14 tumor necrosis factor receptor superfamily member 14 ifnb1 interferon1 tirap tollinterleukin 1 receptor domain containing adaptor protein il2ra interleukin 2 receptor ets1 vets avian erythroblastosis virus e26 oncogene homolog 1 gadd45b growth arrest and dnadamageinducible 45 birc3 baculoviral iap repeat containing 3 vav1 vav 1 guanine nucleotide exchange factor col5a1 collagen type v 1 and c3 complement component 3 have not been investigated thoroughly in the process of periodontitis these genes are mainly involved in bacterial infection immune response and inflammatory reaction suggesting that further characterizing their roles in periodontitis will be importantconclusions a combination of computational tools will be useful in mining candidate genes for periodontitis these theoretical results provide new clues for experimental biologists to plan targeted experiments