DNA甲基化数据的二项障碍谱学习
Spectral Learning of Binomial HMMs for DNA Methylation Data
摘要：
我们考虑二项隐马尔可夫模型的学习参数，其可用于对DNA甲基化数据进行建模。该问题的标准算法是EM，这是计算昂贵的哺乳动物基因组的规模的序列。最近开发的谱算法可以通过张量分解来学习潜在变量模型的参数，并且对于大数据非常高效。然而，这些方法只应用于类别Hynomial，主要的挑战是如何将它们扩展到Binomial Hynomial，同时仍然保持计算效率。我们通过引入一种新的基于特征图的方法来解决这一挑战，该方法利用了二项式延迟的特定属性。我们为我们的算法提供了理论性能保证，并在真实的DNA甲基化数据上进行了评估。
英文摘要：
We consider learning parameters of Binomial Hidden Markov Models, which may be used to model DNA methylation data. The standard algorithm for the problem is EM, which is computationally expensive for sequences of the scale of the mammalian genome. Recently developed spectral algorithms can learn parameters of latent variable models via tensor decomposition, and are highly efficient for large data. However, these methods have only been applied to categorial HMMs, and the main challenge is how to extend them to Binomial HMMs while still retaining computational efficiency. We address this challenge by introducing a new feature-map based approach that exploits specific properties of Binomial HMMs. We provide theoretical performance guarantees for our algorithm and evaluate it on real DNA methylation data.
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