A prioritization analysis of disease association by data-mining of functional annotation of human genes

Takayuki Taniya, Susumu Tanaka, Yumi Yamaguchi-Kabata, Hideki Hanaoka, Chisato Yamasaki, Harutoshi Maekawa, Roberto A. Barrero, Boris Lenhard, Milton W. Datta, Mary Shimoyama, Roger Bumgarner, Ranajit Chakraborty, Ian Hopkinson, Libin Jia, Winston Hide, Charles Auffray, Shinsei Minoshima, Tadashi Imanishi*, Takashi Gojobori

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Complex diseases result from contributions of multiple genes that act in concert through pathways. Here we present a method to prioritize novel candidates of disease-susceptibility genes depending on the biological similarities to the known disease-related genes. The extent of disease-susceptibility of a gene is prioritized by analyzing seven features of human genes captured in H-InvDB. Taking rheumatoid arthritis (RA) and prostate cancer (PC) as two examples, we evaluated the efficiency of our method. Highly scored genes obtained included TNFSF12 and OSM as candidate disease genes for RA and PC, respectively. Subsequent characterization of these genes based upon an extensive literature survey reinforced the validity of these highly scored genes as possible disease-susceptibility genes. Our approach, Prioritization ANalysis of Disease Association (PANDA), is an efficient and cost-effective method to narrow down a large set of genes into smaller subsets that are most likely to be involved in the disease pathogenesis.

Original languageEnglish (US)
Pages (from-to)1-9
Number of pages9
Issue number1
StatePublished - Jan 2012
Externally publishedYes


  • Data-mining
  • Discriminant analysis
  • Disease
  • Gene function
  • Prostate cancer
  • Rheumatoid arthritis

ASJC Scopus subject areas

  • Genetics


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