De Nicolao G, De Nicolao A
A. Castagnetti S.p.A., Milano, Italy.
Comput Methods Programs Biomed. 1995 Aug;47(3):237-52. doi: 10.1016/0169-2607(95)01684-l.
The estimation of the glandular secretory rate from time-series of hormone concentration in plasma can be formulated as a deconvolution problem. In particular, the paper addresses the analysis of frequently sampled data collected in order to study spontaneous pulsatile secretion. Standard deconvolution methods do not allow for the non-negativity constraint and the presence of high-frequency components in the secretory rate. In order to overcome the intrinsic ill-conditioning of the problem, the maximum entropy method is used to obtain a probabilistic representation of the prior knowledge concerning the unknown secretory signal, thus leading to a White Exponential Noise (WEN) model. The deconvolution problem is then posed within a Bayesian framework and solved by means of Maximum-A-Posteriori estimation. The program that implements the algorithm handles non-negativity constraints, provides confidence intervals, and is computationally and memory efficient.
根据血浆中激素浓度的时间序列来估计腺体分泌率，可将其表述为一个反卷积问题。具体而言，本文探讨了为研究自发性脉冲式分泌而收集的频繁采样数据的分析。标准反卷积方法无法考虑分泌率中的非负性约束以及高频成分的存在。为了克服该问题固有的病态性，采用最大熵方法来获得关于未知分泌信号的先验知识的概率表示，从而得到一个白指数噪声（WEN）模型。然后在贝叶斯框架内提出反卷积问题，并通过最大后验估计来求解。实现该算法的程序处理非负性约束，提供置信区间，并且在计算和内存方面都很高效。
