WENDEC: a deconvolution program for processing hormone time-series.
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.
De Nicolao
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G
A. Castagnetti S.p.A., Milano, Italy.
De Nicolao
A
A
D000465:Algorithms; D001499:Bayes Theorem; D015982:Bias; D016001:Confidence Intervals; D006728:Hormones / Q000097:blood* / Q000378:metabolism*; D006801:Humans; D015233:Models, Statistical; D009716:Numerical Analysis, Computer-Assisted; D011673:Pulsatile Flow; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D012986:Software Validation; D013997:Time Factors
D006728:Hormones