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Segway 2.0: Gaussian mixture models and minibatch training

View ORCID ProfileRachel C.W. Chan, View ORCID ProfileMaxwell W. Libbrecht, View ORCID ProfileEric G. Roberts, View ORCID ProfileWilliam Stafford Noble, View ORCID ProfileMichael M. Hoffman
doi: https://doi.org/10.1101/147470
Rachel C.W. Chan
1Princess Margaret Cancer Centre, Toronto, Ontario, Canada
2Engineering Physics Program, University of British Columbia, Vancouver, British Columbia, Canada
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Maxwell W. Libbrecht
3Department of Computer Science and Engineering, University of Washington, Seattle, Washington, United States
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Eric G. Roberts
1Princess Margaret Cancer Centre, Toronto, Ontario, Canada
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William Stafford Noble
3Department of Computer Science and Engineering, University of Washington, Seattle, Washington, United States
4Department of Genome Sciences, University of Washington, Seattle, Washington, United States
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Michael M. Hoffman
1Princess Margaret Cancer Centre, Toronto, Ontario, Canada
5Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
6Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
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Abstract

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 https://segway.hoffmanlab.org. We have made available scripts (https://doi.org/10.5281/zenodo.802940) and datasets (https://doi.org/10.5281/zenodo.802907) for this paper’s analysis.

Contact michael.hoffman{at}utoronto.ca

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted June 08, 2017.
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Segway 2.0: Gaussian mixture models and minibatch training
Rachel C.W. Chan, Maxwell W. Libbrecht, Eric G. Roberts, William Stafford Noble, Michael M. Hoffman
bioRxiv 147470; doi: https://doi.org/10.1101/147470
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Segway 2.0: Gaussian mixture models and minibatch training
Rachel C.W. Chan, Maxwell W. Libbrecht, Eric G. Roberts, William Stafford Noble, Michael M. Hoffman
bioRxiv 147470; doi: https://doi.org/10.1101/147470

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