遗传数据数据压缩的上下文分箱、模型聚类和自适应性
Context binning, model clustering and adaptivity for data compression of genetic data
摘要：
基因数据库的快速增长意味着通过改进其数据压缩可以节省大量资金，这需要更好的廉价统计模型。这篇文章提出了自动化的优化，例如马尔可夫类模型，特别是上下文分箱和模型聚类。虽然只删除上下文的低位是很流行的，但提出的上下文分箱自动优化了这种简化，如表：state=bin[context]确定概率分布，这种方式也从非常大的上下文中提取几乎所有有用的信息，进入相对较少的状态。第二种拟议办法：模型聚类在一般统计模型的空间中使用k均值聚类，允许优化例如为每个读段单独选择的几个模型（作为聚类质心）。还简要讨论了一些自适应技术，包括数据的非平稳性。
英文摘要：
Rapid growth of genetic databases means huge savings from improvements in their data compression, what requires better inexpensive statistical models. This article proposes automatized optimizations e.g. of Markov-like models, especially context binning and model clustering. While it is popular to just remove low bits of the context, proposed context binning automatically optimizes such reduction as tabled: state=bin[context] determining probability distribution, this way extracting nearly all useful information also from very large contexts, into a relatively small number of states. The second proposed approach: model clustering uses k-means clustering in space of general statistical models, allowing to optimize a few models (as cluster centroids) to be chosen e.g. separately for each read. There are also briefly discussed some adaptivity techniques to include data non-stationarity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
