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Publisher: Biomedical Informatics Publishing Group
Journal: Bioinformation
Languages: English
Types: Article
Subjects: Prediction Model, beta barrel transmembrane protein, prokaryotic membrane proteins, prediction method, subcellular location, Bayesian Networks

Classified by OpenAIRE into

ACM Ref: ComputingMethodologies_PATTERNRECOGNITION, Hardware_ARITHMETICANDLOGICSTRUCTURES
Identifiers:pmc:PMC1891705
We describe a novel and potentially important tool for candidate subunit vaccine selection through in silico reverse-vaccinology. A set of Bayesian networks able to make individual predictions for specific subcellular locations is implemented in three pipelines with different architectures: a parallel implementation with a confidence level-based decision engine and two serial implementations with a hierarchical decision structure, one initially rooted by prediction between membrane types and another rooted by soluble versus membrane prediction. The parallel pipeline outperformed the serial pipeline, but took twice as long to execute. The soluble-rooted serial pipeline outperformed the membrane-rooted predictor. Assessment using genomic test sets was more equivocal, as many more predictions are made by the parallel pipeline, yet the serial pipeline identifies 22 more of the 74 proteins of known location.

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