ArticleBMC bioinformatics2009
Robust methods for accurate diagnosis using pan-microbiological oligonucleotide microarrays.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 11 citations in OpenAlex.
- In Silico cancer cell versus stroma cellularity index computed from species-specific human and mouse transcriptome of xenograft models: towards accurate stroma targeting therapy assessment.BMC medical genomics · 2014Article
- Single sample expression-anchored mechanisms predict survival in head and neck cancer.PLoS computational biology · 2012Article
- Stromal microenvironment processes unveiled by biological component analysis of gene expression in xenograft tumor models.BMC bioinformatics · 2010Article
- Selected proceedings of the First Summit on Translational Bioinformatics 2008.BMC bioinformatics · 2009Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundTo 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.
methodsIn 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.
resultsThough 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.
conclusionThe proposed multi-specie array analysis based on the hypergeometric distribution addresses shortcomings of previous methods by enhancing signals of positively hybridized probes.
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