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dc.contributor.authorYuan, Xiao-Tong
dc.contributor.authorZhang, Tong
dc.contributor.authorWan, Xiu-Feng
dc.date.accessioned2015-10-09T19:50:50Z
dc.date.available2015-10-09T19:50:50Z
dc.date.issued7/25/2013
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/11668/2512
dc.identifier.urihttp://dx.doi.org/10.1371/journal.pone.0069842
dc.description.abstractAntigenic characterization based on serological data, such as Hemagglutination Inhibition (HI) assay, is one of the routine procedures for influenza vaccine strain selection. In many cases, it would be impossible to measure all pairwise antigenic correlations between testing antigens and reference antisera in each individual experiment. Thus, we have to combine and integrate the HI tables from a number of individual experiments. Measurements from different experiments may be inconsistent due to different experimental conditions. Consequently we will observe a matrix with missing data and possibly inconsistent measurements. In this paper, we develop a new mathematical model, which we refer to as Joint Matrix Completion and Filtering, for HI data integration. In this approach, we simultaneously handle the incompleteness and uncertainty of observations by assuming that the underlying merged HI data matrix has low rank, as well as carefully modeling different levels of noises in each individual table. An efficient blockwise coordinate descent procedure is developed for optimization. The performance of our approach is validated on synthetic and real influenza datasets. The proposed joint matrix completion and filtering model can be adapted as a general model for biological data integration, targeting data noises and missing values within and across experiments.
dc.publisherPublic Library of Science
dc.relation.ispartofseriesPLoS ONE (Volume 8, Issue 7)
dc.subject.otherAnimals
dc.subject.otherAntigens
dc.subject.otherBirds
dc.subject.otherBirds: immunology
dc.subject.otherBirds: virology
dc.subject.otherHemagglutination Inhibition Tests
dc.subject.otherHuman
dc.subject.otherHuman: immunology
dc.subject.otherHuman: prevention & control
dc.subject.otherHumans
dc.subject.otherImmune Sera
dc.subject.otherImmune Sera: analysis
dc.subject.otherImmune Sera: immunology
dc.subject.otherImmunological
dc.subject.otherInfluenza
dc.subject.otherInfluenza in Birds
dc.subject.otherInfluenza in Birds: immunology
dc.subject.otherInfluenza in Birds: prevention & control
dc.subject.otherInfluenza Vaccines
dc.subject.otherInfluenza Vaccines: administration & dosage
dc.subject.otherInfluenza Vaccines: chemistry
dc.subject.otherInfluenza Vaccines: immunology
dc.subject.otherModels
dc.subject.otherObserver Variation
dc.subject.otherOrthomyxoviridae
dc.subject.otherOrthomyxoviridae: immunology
dc.subject.otherSignal-To-Noise Ratio
dc.subject.otherStatistical
dc.subject.otherViral
dc.subject.otherViral: chemistry
dc.subject.otherViral: immunology
dc.titleA joint matrix completion and filtering model for influenza serological data integration.
dc.typeArticle
dc.publisher.departmentDepartment of Basic Sciences
dc.publisher.collegeCollege of Veterinary Medicine
dc.identifier.doi10.1371/journal.pone.0069842


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