Time Series Modeling of Nano-Gold Immunochromatographic Assay via Expectation Maximization Algorithm
IEEE Transactions on Biomedical EngineeringPublished 25 April 2013
Nianyin Zeng, Zidong Wang, Yurong Li, Min Du, Jie Cao, Xiaohui Liu
Citations30
SJR quartileQ1
SJR score1.11
SNIP1.58
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TL;DR
The expectation maximization (EM) algorithm is applied to the modeling of the nano-gold immunochromatographic assay (nano-GICA) via available time series of the measured signal intensities of the test and control lines, showing that the model fits the data very well.
Abstract
In this paper, the expectation maximization (EM) algorithm is applied to the modeling of the nano-gold immunochromatographic assay (nano-GICA) via available time series of the measured signal intensities of the test and control lines. The model for the nano-GICA is developed as the stochastic dynamic model that consists of a first-order autoregressive stochastic dynamic process and a noisy measurement. By using the EM algorithm, the model parameters, the actual signal intensities of the test and control lines, as well as the noise intensity can be identified simultaneously. Three different time series data sets concerning the target concentrations are employed to demonstrate the effectiveness of the introduced algorithm. Several indices are also proposed to evaluate the inferred models. It is shown that the model fits the data very well.
Keywords
Biochemistry, Genetics and Molecular BiologyEngineering
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