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Segway 2.0: Gaussian mixture models and minibatch training

SUMMARY: Segway performs semi-automated genome annotation, discovering joint patterns across multiple genomic signal datasets. We discuss a major new version of Segway and highlight its ability to model data with substantially greater accuracy. Major enhancements in Segway 2.0 include the ability to...

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Detalles Bibliográficos
Autores principales: Chan, Rachel C W, Libbrecht, Maxwell W, Roberts, Eric G, Bilmes, Jeffrey A, Noble, William Stafford, Hoffman, Michael M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860603/
https://www.ncbi.nlm.nih.gov/pubmed/29028889
http://dx.doi.org/10.1093/bioinformatics/btx603
Descripción
Sumario:SUMMARY: Segway performs semi-automated genome annotation, discovering joint patterns across multiple genomic signal datasets. We discuss a major new version of Segway and highlight its ability to model data with substantially greater accuracy. Major enhancements in Segway 2.0 include the ability to model data with a mixture of Gaussians, enabling capture of arbitrarily complex signal distributions, and minibatch training, leading to better learned parameters. AVAILABILITY AND IMPLEMENTATION: Segway and its source code are freely available for download at http://segway.hoffmanlab.org. We have made available scripts (https://doi.org/10.5281/zenodo.802939) and datasets (https://doi.org/10.5281/zenodo.802906) for this paper’s analysis. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.