Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a framework for the simultaneous estimation of state and parameters of partially observed Boolean dynamical systems (POBDS). Simultaneous state and parameter estimation is achieved through the combined use of the Boolean Kalman filter and Boolean Kalman smoother, which provide the minimum mean-square error state estimators for the POBDS model, and maximum-likelihood (ML) parameter estimation; in the presence of continuous parameters, ML estimation is performed using the expectation–maximization algorithm. The performance of the proposed ML adaptive filter is demonstrated by numerical experiments with a POBDS model of gene regulatory networks observed through noisy next-generation sequencing (RNA-seq) time series data using the ll-known p53-MDM2 negative-feedback loop gene regulatory model.
['Mahdi Imani', 'Ulisses Braga-Neto']
I am writing a research paper on a idea Maximum-Likelihood Adaptive Filter for Partially Observed Boolean Dynamical Systems
[{"from": "human", "value": "I am writing a research paper on a idea Maximum-Likelihood Adaptive Filter for Partially Observed Boolean Dynamical Systems"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a framework for the simultaneous estimation of state and parameters of partially observed Boolean dynamical systems (POBDS). Simultaneous state and parameter estimation is achieved through the combined use of the Boolean Kalman filter and Boolean Kalman smoother, which provide the minimum mean-square error state estimators for the POBDS model, and maximum-likelihood (ML) parameter estimation; in the presence of continuous parameters, ML estimation is performed using the expectation\u2013maximization algorithm. The performance of the proposed ML adaptive filter is demonstrated by numerical experiments with a POBDS model of gene regulatory networks observed through noisy next-generation sequencing (RNA-seq) time series data using the ll-known p53-MDM2 negative-feedback loop gene regulatory model."}]