期刊文献+

用ENDEAVOUR软件鉴定肥胖症致病基因GAD2 被引量:2

Verification of candidate gene GAD2 for human obesity by ENDEAVOUR
原文传递
导出
摘要 鉴别复杂疾病致病基因对于治疗和预防疾病作用非常重要,致病基因预测软件是鉴别致病基因的有效工具。位于染色体10p12的基因GAD2是尚有争议的肥胖基因,许多研究者用实验方法研究基因GAD2,却得出了不一致的结论。基于生物计算的致病基因预测软件能弥补实验的不足,已成为揭示疾病发病机理和预测致病基因的有效途径,本文采用ENDEAVOUR软件来验证基因GAD2是否为肥胖症致病基因,该软件运用多种生物数据,根据每一个候选基因与已知致病基因的相似程度以排序,结果在35个候选基因中,基因GAD2排在最前面,表明基因GAD2最可能是肥胖症致病基因。预测结果有利于重新解释基因GAD2的生物功能,促使肥胖症药物的研制及治疗水平的提高。 Disease genes identification is very important for understanding disease pathogenesis and improving clinic treatment. The computational gene prioritization tools have the advantages of huge data sources, efficient data processing and low costs. Many researchers have studied the disputing candidate disease gene GAD2 for obesity on chromosome 10p12 by association studies but obtained contradictory conclusions. In this paper, the probability of gene GAD2 being an obesity disease gene is verified by the computational gene prioritization tool ENDEAVOUR. The prioritization strategies of ENDEAVOUR are based on the similarity between the candidate gene and the profile derived from genes already known to be involved in the processes (the training genes) by genomic data fusion. In the computational results, the first rank of gene GAD2 means that the probability of gene GAD2 being an obesity disease gene is high. The results are helpful to explain the biological mechanism of gene GAD2 and genetic factors of the complex obesity disease.
出处 《计算机与应用化学》 CAS CSCD 北大核心 2011年第10期1244-1248,共5页 Computers and Applied Chemistry
基金 南京林业大学高学历人才基金
关键词 致病基因 肥胖症 预测软件 disease genes, obesity, ENDEAVOUR
  • 相关文献

参考文献1

二级参考文献4

共引文献6

同被引文献63

  • 1顾萍.如何利用OMIM获取人类基因与遗传失调信息[J].生命科学,2004,16(5):330-332. 被引量:3
  • 2Sassi C, Guerreiro R, Gibbs R, et al. Investigating the role of rare coding variability in Mendelian dementia genes (APP, PSEN1, PSEN2, GRN, MAPT, and PRNP) in late-onset A1- zheimer's disease [J]. Neurobiol Aging, 2014, 35(12): 2881-2886.
  • 3Floudas CS, Um N, Kamboh MI, et al. Identifying genetic inter- actions associated with late-onset Alzheimer's disease [J]. Bio- Data Min, 2014, 7(1): 35-54.
  • 4Zou Z, Liu C, Che C, et al. Clinical genetics of Alzheimer's dis- ease [J]. Biomed Res Int, 2014, 2014(1): 862-872.
  • 5Aerts S, Lambrechts D, Maity S, et al. Gene prioritization through genomic data fusion [J]. Nat Biotechnol, 2006, 24(6): 719-719.
  • 6Yu W, Wulf A, Liu TB, et al. Gene Prospector: An evidence gateway for evaluating potential susceptibility genes and inter- acting risk factors for human diseases [J]. BMC Bioinformat- ics, 2008, 9: 528.
  • 7Jourquin J, Duncan D, Shi Z, et al. GLAD4U: deriving and pri- oritizing gene lists from PubMed literature [J]. BMC Genom- ics, 2012, 13(Suppl 8): S20.
  • 8Martinez V, Cano C, Blanco A. ProphNet: A generic prioritiza- tion method through propagation of information [J]. BMC Bio- informatics, 2014, 15(Suppl 1): S5.
  • 9Bertram L, McQueen MB, Mullin K, et al. Systematic me- ta-analyses of Alzheimer disease genetic association studies: the AlzGene database [J]. Nat Genet, 2007, 39(1): 17-23.
  • 10Olgiati P, Politis AM, Papadimitriou GN, et al. Genetics of late-onset Alzheimer's disease: update from the alzgene data- base and analysis of shared pathways [J]. Int J Alzheimers Dis, 2011, 10(1): 4061-4075.

引证文献2

二级引证文献2

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部