赛格威 2.0：高斯混合模型和小批量训练。
Segway 2.0: Gaussian mixture models and minibatch training.
机构信息
Princess Margaret Cancer Centre, Toronto, ON M5G 1L7, Canada.
Engineering Physics Program, University of British Columbia, Vancouver, BC V6T 1Z1, Canada.
出版信息
Bioinformatics. 2018 Feb 15;34(4):669-671. doi: 10.1093/bioinformatics/btx603.
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.
CONTACT
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
摘要
Segway 执行半自动基因组注释，发现多个基因组信号数据集之间的联合模式。我们讨论了 Segway 的一个主要新版本，并强调了它能够更准确地对数据进行建模的能力。Segway 2.0 的主要增强功能包括能够使用混合高斯模型对数据进行建模，从而能够捕获任意复杂的信号分布，以及使用小批量训练，从而获得更好的学习参数。
可用性和实现
Segway 及其源代码可在 http://segway.hoffmanlab.org 免费下载。我们提供了用于本文分析的脚本（https://doi.org/10.5281/zenodo.802939）和数据集（https://doi.org/10.5281/zenodo.802906）。
联系人
补充信息
补充数据可在“Bioinformatics”在线获取。
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