VIPR: A probabilistic algorithm for analysis of microbial detection microarrays

Background: All infectious disease oriented clinical diagnostic assays in use today focus on detecting the presence of a single, well defined target agent or a set of agents. In recent years, microarray-based diagnostics have been developed that greatly facilitate the highly parallel detection of multiple microbes that may be present in a given clinical specimen. While several algorithms have been described for interpretation of diagnostic microarrays, none of the existing approaches is capable of incorporating training data generated from positive control samples to improve performance. Results: To specifically address this issue we have developed a novel interpretive algorithm, VIPR (Viral Identification using a PRobabilistic algorithm), which uses Bayesian inference to capitalize on empirical training data to optimize detection sensitivity. To illustrate this approach, we have focused on the detection of viruses that cause hemorrhagic fever (HF) using a custom HF-virus microarray. ...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Adam F. Allred, Guang Wu, Tuya Wulan, Kael F. Fischer, Michael R. Holbrook, Robert B. Tesh, David Wang
• Keywords: Algorithm, BMCBI 2010, Positive Control Samples, Positive Controls