期刊
IEEE TRANSACTIONS ON SIGNAL PROCESSING
卷 65, 期 2, 页码 359-371出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2016.2614798
关键词
Adaptive filtering; partially-observed Boolean dynamical systems; maximum-likelihood estimation; Boolean Kalman filter; expectation maximization; gene regulatory networks; RNA-seq data
资金
- National Science Foundation through NSF [CCF-1320884]
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [1320884] Funding Source: National Science Foundation
We 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 well-known p53-MDM2 negative-feedback loop gene regulatory model.
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