Inference of Model Parameters Using Particle Filter Algorithm and Copula Distributions.
Autor: Deng, Zhimin; Zhang, Xinan; Tian, Tianhai
Publication year: 2020
IEEE/ACM transactions on computational biology and bioinformatics
issn:1557-9964 1545-5963
doi: 10.1109/TCBB.2018.2880974
Abstract:
It is widely accepted that experimental data often include noise because of the limitation in experimental conditions. In addition, biological systems inside the cells also contain uncertainty due to small copy molecular numbers. To address this issue, it was proposed that experimental data include both real system state and a noise term whose variance is a constant. An additional assumption is that the observation data of different variables are independent to each other. However, recent research works showed that noise in experimental data might not be the white noise. In addition, the observed values of different variables may be correlated. This work designs a new algorithm to infer the unknown model parameters based on noisy data. The innovation of this method includes a new noise model, in which the variance of noise is dependent on the system state, and a copula particle filter algorithm that uses the copula density functions to describe the dependence of different variables. The proposed algorithm is evaluated by using two deterministic models for gene networks and a stochastic model. Numerical results show that the accuracy of our proposed method is better than that of the widely used Liu-West filter and copula particle filter algorithms.
Language: eng
Rights:
Pmid: 30418916
Tags: *Algorithms; *Models, Statistical; Transcription Factors/genetics; Computational Biology/*methods; Gene Regulatory Networks/*genetics
Link: https://pubmed.ncbi.nlm.nih.gov/30418916/
