分析叠加信号之间的串扰：向量范数依赖的隐马尔可夫模型及其在离子通道中的应用
Analyzing cross-talk between superimposed signals: Vector norm dependent hidden Markov models and applications to ion channels
AI总结：
提出一种向量范数依赖的隐马尔可夫模型，用于分析无法单独观测的叠加两态信号记录，解决了离子通道串扰分析中的依赖结构建模与参数可识别性问题，并在真实心肌数据上验证了有效性。
摘要：
我们提出并研究了一种隐马尔可夫模型（HMM），用于分析具有依赖性、聚合且叠加的两态信号记录。这项工作的一个主要动机是，这些信号通常无法单独观测，只能观测到它们的叠加。除其他应用外，这类模型在理解离子通道之间的串扰方面需求很大，因为每个单独的通道无法被单独测量。作为核心基础模块，我们引入了一个参数化的向量范数依赖马尔可夫链模型，并从排列不变性以及条件独立性的角度对其进行了刻画。该基础模块导出了一个隐马尔可夫链求和过程，可用于在HMM框架内分析叠加两态信号观测的依赖结构。值得注意的是，向量范数依赖马尔可夫链的模型参数由求和过程的参数唯一确定，因此是可识别的。我们提供了估计参数的算法，讨论了模型选择，并将我们的方法应用于来自心肌的真实离子通道数据，展示了竞争性门控现象。
英文摘要：
We propose and investigate a hidden Markov model (HMM) for the analysis of dependent, aggregated, superimposed two-state signal recordings. A major motivation for this work is that often these signals cannot be observed individually but only their superposition. Among others, such models are in high demand for the understanding of cross-talk between ion channels, where each single channel cannot be measured separately. As an essential building block, we introduce a parameterized vector norm dependent Markov chain model and characterize it in terms of permutation invariance as well as conditional independence. This building block leads to a hidden Markov chain sum process which can be used for analyzing the dependence structure of superimposed two-state signal observations within an HMM. Notably, the model parameters of the vector norm dependent Markov chain are uniquely determined by the parameters of the sum process and are therefore identifiable. We provide algorithms to estimate the parameters, discuss model selection and apply our methodology to real-world ion channel data from the heart muscle, where we show competitive gating.
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。
