Robust methods for accurate diagnosis using pan-microbiological oligonucleotide microarrays.

Yang Liu, Lee Sam, Jianrong Li, Yves A. Lussier

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


BACKGROUND: To address the limitations of traditional virus and pathogen detection methodologies in clinical diagnosis, scientists have developed high-throughput oligonucleotide microarrays to rapidly identify infectious agents. However, objectively identifying pathogens from the complex hybridization patterns of these massively multiplexed arrays remains challenging. METHODS: In this study, we conceived an automated method based on the hypergeometric distribution for identifying pathogens in multiplexed arrays and compared it to five other methods. We evaluated these metrics: 1) accurate prediction, whether the top ranked prediction(s) match the real virus(es); 2) four accuracy scores. RESULTS: Though accurate prediction and high specificity and sensitivity can be achieved with several methods, the method based on hypergeometric distribution provides a significant advantage in term of positive predicting value with two to sixty folds the positive predicting values of other methods. CONCLUSION: The proposed multi-specie array analysis based on the hypergeometric distribution addresses shortcomings of previous methods by enhancing signals of positively hybridized probes.

Original languageEnglish (US)
Pages (from-to)S11
JournalBMC bioinformatics
Volume10 Suppl 2
StatePublished - 2009

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics


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